# Getting started with wrMisc

## Introduction

This package contains a collection of various (low-level) tools which may be of general interest. These functions were accumulated over a number of years of data-wrangling when treating high-throughput data from biomedical applications. Besides, these functions are further used/integrated in more specialized functions dedicated to specific applications in the packages wrProteo, wrGraph or wrTopDownFrag. All these packages are available on CRAN.

If you are not familiar with R you may find many introductory documents on the official R-site in contributed documents or under Documentation/Manuals. Of course, numerous other documents/sites with tutorials and courses exist, too.

### Dependencies and Compilation

One of the aims was to write a package easy to install, with low system requirements and few obligatory dependencies.
All code is written in pure R and does not need any special compilers. The number of obligatory dependencies was kept to a minumum.

Most of additional packages used in some of the functions were declared as ‘suggested’ (ie not obligatory), to allow installation of wrMisc even if some these additional packages can’t be installed/compiled by the user’s instance. When a feature/function of one of the ‘suggested’ packages is about to be used, its presence/installation will be checked and, only if found as missing, the user will be prompted a message inviting to install specific package(s) before using these specific functions. This helps to avoid not being able installing this package at all if some dependencies may fail to get installed themselves.

To get started, we need to install (if not yet installed) and load the package “wrMisc” available from CRAN.

## If not already installed, you'll have to install the package first.
## This is the basic installation commande in R
install.packages("wrMisc")

Since the functions illustrated in this vignette require a number of the suggested packages, let’s check if they are installed and add them (via a small function), if not yet installed.

packages <- c("knitr", "rmarkdown", "BiocManager", "kableExtra", "boot", "data.tree", "data.table",
"fdrtool", "RColorBrewer", "Rcpp", "wrMisc", "wrGraph", "wrProteo")
checkInstallPkg <- function(pkg) {       # install function
if(!requireNamespace(pkg, quietly=TRUE)) install.packages(pkg) }

## install if not yet present
sapply(packages, checkInstallPkg)

Finally, this package also uses the Bioconductor package limma which has to be installed differently (see also help on Bioconductor):

## Installation of limma
BiocManager::install("limma")

This vignette is also accessible from R command-line or on CRAN at wrMisc:

## Now you can open this vignette out of R:
vignette("wrMiscVignette1", package="wrMisc")

Before using the functions of this package, we actually need to load the package first (best on a fresh R-session):

library("wrMisc")
library("knitr")

## This is 'wrMisc' version number :
packageVersion("wrMisc")
## [1] '1.10.0'

## Speed Optimized Functions In The Package wrMisc

In high-throughput experiments in biology (like transcriptomics, proteomics etc…) many different features get measured a number if times (different samples like patients or evolution of a disease). The resulting data typically contain many (independent) rows (eg >1000 different genes or proteins who’s abundance was measured) and much fewer columns that may get further organized in groups of replicates. As R is a versatile language, multiple options exist for assessing the global characteristics of such data, some are more efficient on a computational point of view. In order to allow fast treatment of very large data-sets some tools have been re-designed for optimal performance.

### Assessing Basic Information About Variability (for matrix)

Many mesurement techniques applied in high throughput manner suffer from precision. This means, the same measurements taken twice in a row (ie repeated on the same subject) will very likely not give an identical result. For this reason it is common practice to make replicate measurements to i) estimate mean (ie representative) values and ii) asses the factors contributing to the variablity observed. Briefly, technical replicates represent the case where multiple read-outs of the very same sample are generated and the resulting variability is associated to technical issues during the process of taking measures. Biological replicates represent independant samples and reflect therefore the varibility a given parameter may have in a certain population of individuals. With the tools presented here, both technical and biological replicates can be dealt with. In several cases the interpretation of the resulting numbers should consider the experimental setup, though.

Let’s make a simple matrix as toy data:

grp1 <- rep(LETTERS[1:3], c(3,4,3))
sampNa1 <- paste0(grp1, c(1:3,1:4,1:3))
set.seed(2016); dat1 <- matrix(round(c(runif(50000) +rep(1:1000,50)),3),
ncol=10, dimnames=list(NULL,sampNa1))
dim(dat1)
## [1] 5000   10
head(dat1)
##         A1    A2    A3    B1    B2    B3    B4    C1    C2    C3
## [1,] 1.180 1.640 1.199 1.118 1.425 1.745 1.253 1.554 1.303 1.856
## [2,] 2.143 2.237 2.730 2.693 2.603 2.293 2.542 2.452 2.148 2.776
## [3,] 3.842 3.155 3.191 3.520 3.686 3.408 3.409 3.871 3.345 3.588
## [4,] 4.134 4.394 4.982 4.320 4.380 4.888 4.965 4.462 4.250 4.647
## [5,] 5.478 5.472 5.488 5.570 5.626 5.765 5.551 5.016 5.659 5.139
## [6,] 6.121 6.294 6.718 6.890 6.999 6.316 6.542 6.119 6.763 6.487

Now lets estimate the standard deviation (sd) for every row:

head(rowSds(dat1))
## [1] 0.2583693 0.2426026 0.2477899 0.3089102 0.2307722 0.3124493
system.time(sd1 <- rowSds(dat1))
##    user  system elapsed
##       0       0       0
system.time(sd2 <- apply(dat1,1,sd))
##    user  system elapsed
##    0.09    0.00    0.10

On most systems the equivalent calculation using apply() will run much slower compared to rowSds.

Note, there is a minor issue with rounding :

table(round(sd1,13)==round(sd2,13))
##
## FALSE  TRUE
##     1  4999

Similarly we can easily calculate the CV (coefficient of variation, ie sd / mean, see also CV) for every row using rowCVs :

system.time(cv1 <- rowCVs(dat1))
##    user  system elapsed
##       0       0       0
system.time(cv2 <- apply(dat1,1,sd)/rowMeans(dat1))
##    user  system elapsed
##    0.08    0.00    0.08
# typically the calculation using rowCVs is much faster
head(cv1)
## [1] 0.18101959 0.09855083 0.07076678 0.06800894 0.04213940 0.04788568
# results from the 'conventional' way
head(cv2)
## [1] 0.18101959 0.09855083 0.07076678 0.06800894 0.04213940 0.04788568

Note, these calculations will be very efficient as long as the number of rows is much higher (>>) than the number of columns.

### Data Organized In (Sub-)Groups As Sets Of Columns

Now, let’s assume our data is contains 3 initial samples measured as several replicates (already defined in grp1). Similarly, we can also calculate the sd or CV for each line while splitting into groups of replicates (functions rowGrpMeans, rowGrpSds and rowGrpCV):

# we already defined the grouping :
grp1
##  [1] "A" "A" "A" "B" "B" "B" "B" "C" "C" "C"
## the mean for each group and row
system.time(mean1Gr <- rowGrpMeans(dat1, grp1))
##    user  system elapsed
##    0.01    0.00    0.01
## Now the sd for each row and group
system.time(sd1Gr <- rowGrpSds(dat1, grp1))
##    user  system elapsed
##    0.02    0.00    0.02
# will give us a matrix with the sd for each group & line
head(sd1Gr)
##                A          B         C
## [1,] 0.260269732 0.27074758 0.2768917
## [2,] 0.315291928 0.17144557 0.3140531
## [3,] 0.386666523 0.13115989 0.2632534
## [4,] 0.434443706 0.33521970 0.1986530
## [5,] 0.008082904 0.09672297 0.3413156
## [6,] 0.307168249 0.31475851 0.3230934
# Let's check the results of the first line :
sd1Gr[1,] == c(sd(dat1[1,1:3]), sd(dat1[1,4:7]), sd(dat1[1,8:10]))
##    A    B    C
## TRUE TRUE TRUE
# The CV :
system.time(cv1Gr <- rowGrpCV(dat1, grp1))
##    user  system elapsed
##       0       0       0
head(cv1Gr)
##                A          B          C
## [1,] 0.194279471 0.19545033 0.17625186
## [2,] 0.133034569 0.06769147 0.12773308
## [3,] 0.113859400 0.03741279 0.07309886
## [4,] 0.096471585 0.07227288 0.04461104
## [5,] 0.001475162 0.01718603 0.06474939
## [6,] 0.048163108 0.04707197 0.05004286

#### Counting Number Of NAs Per Row And Group Of Columns

Some data, like with quantitative proteomics measures, may contain an elevated number of NAs (see also the package wrProteo for further options for dealing with such data). Furthermore, many other packages on CRAN and Bioconductor cover this topic, see also the missing data task-view on CRAN. Similar as above there is an easy way to count the number of NAs to get an overview how NAs are distributed.

Let’s assume we have measures from 3 groups/samples with 4 replicates each :

mat2 <- c(22.2, 22.5, 22.2, 22.2, 21.5, 22.0, 22.1, 21.7, 21.5, 22, 22.2, 22.7,
NA, NA, NA, NA, NA, NA, NA, 21.2,   NA, NA, NA, NA,
NA, 22.6, 23.2, 23.2,  22.4, 22.8, 22.8, NA,  23.3, 23.2, NA, 23.7,
NA, 23.0, 23.1, 23.0,  23.2, 23.2, NA, 23.3,  NA, NA, 23.3, 23.8)
mat2 <- matrix(mat2, ncol=12, byrow=TRUE)
## The definition of the groups (ie replicates)
gr4 <- gl(3, 4, labels=LETTERS[1:3])

Now we can easily count the number of NAs per row and set of replicates.

rowGrpNA(mat2,gr4)
##      A B C
## [1,] 0 0 0
## [2,] 4 3 4
## [3,] 1 1 1
## [4,] 1 1 2

### Fast NA-omit For Very Large Objects

The function na.omit() from the package stats also keeps a trace of all omitted instances. This can be penalizing in terms of memory usage when handling very large vectors with a high content of NAs (eg >10000 NAs). If you don’t need to document precisely which elements got eliminated, the function naOmit() may offer smoother functioning for very large objects.

aA <- c(11:13,NA,10,NA)

str(naOmit(aA))
##  num [1:4] 11 12 13 10
# the 'classical' na.omit also stores which elements were NA
str(na.omit(aA))
##  num [1:4] 11 12 13 10
##  - attr(*, "na.action")= 'omit' int [1:2] 4 6

### Minimum Distance/Difference Between Values

If you need to find the closest neighbour(s) of a numeric vector, the function minDiff() will tell you the distance (“dif”,“ppm” or “ratio”) and index (“best”) of the closest neighbour. In case of multiple shortest distances the index if the first one is reported, and the column “nbest” will display a value of >1.

set.seed(2017); aa <- 10*c(0.1 +round(runif(20),2), 0.53, 0.53)
head(aa)
## [1] 10.2  6.4  5.7  3.9  8.7  8.7
minDiff(aa,ppm=FALSE)
##       index value  dif   rat ncur nbest best
##  [1,]     1  10.2 -0.2 0.981    1     1   19
##  [2,]     2   6.4  0.4 1.070    1     1   15
##  [3,]     3   5.7  0.3 0.950    2     1   15
##  [4,]     4   3.9  0.2 1.050    1     1   10
##  [5,]     5   8.7  0.5 1.060    2     1   18
##  [6,]     6   8.7  0.5 1.060    2     1   18
##  [7,]     7   1.4  0.1 1.080    1     1   13
##  [8,]     8   5.3  0.3 1.060    4     1   17
##  [9,]     9   5.7  0.3 0.950    2     1   15
## [10,]    10   3.7 -0.2 0.949    1     1    4
## [11,]    11   7.7 -0.5 0.939    1     1   18
## [12,]    12   1.0 -0.3 0.769    1     1   13
## [13,]    13   1.3 -0.1 0.929    1     1    7
## [14,]    14   5.3  0.3 1.060    4     1   17
## [15,]    15   6.0  0.3 1.050    1     2    9
## [16,]    16   4.9 -0.1 0.980    1     1   17
## [17,]    17   5.0  0.1 1.020    1     1   16
## [18,]    18   8.2  0.5 1.060    1     1   11
## [19,]    19  10.4  0.2 1.020    1     1    1
## [20,]    20   9.3  0.6 1.070    1     2    6
## [21,]    21   5.3  0.3 1.060    4     1   17
## [22,]    22   5.3  0.3 1.060    4     1   17

When you look at the first line, the value of 10.2 has one single closest value which is 10.4, which is located in line number 19 (the column ‘best’ gives the index of the best). Line number 19 points back to line number 1. You can see, that some elements (like 5.7) occure multiple times (line no 3 and 9), multiple occurences are counted in the column ncur. This is why column nbest for line 15 (value =6.0) indicates that it appears twice as closest value nbest.

## Working With Lists (And Lists Of Lists)

### Partial unlist

When input from different places gets collected and combined into a list, this may give a collection of different types of data. The function partUnlist() will to preserve multi-column elements as they are (and just bring down one level):

bb <- list(fa=gl(2,2), ve=31:33, L2=matrix(21:28,ncol=2), li=list(li1=11:14,li2=data.frame(41:44)))
partUnlist(bb)
## $fa ## [1] 1 1 2 2 ## Levels: 1 2 ## ##$ve
## [1] 31 32 33
##
## $L2 ## [,1] [,2] ## [1,] 21 25 ## [2,] 22 26 ## [3,] 23 27 ## [4,] 24 28 ## ##$li
## [1] 11 12 13 14
##
## $li ## X41.44 ## 1 41 ## 2 42 ## 3 43 ## 4 44 partUnlist(lapply(bb,.asDF2)) ##$fa
##   as.character(z)
## 1               1
## 2               1
## 3               2
## 4               2
##
## $ve ## V1 ## 1 31 ## 2 32 ## 3 33 ## ##$L2
##   V1 V2
## 1 21 25
## 2 22 26
## 3 23 27
## 4 24 28
##
## $li ## V1 ## li1 11, 12, 13, 14 ## li2 41, 42, 43, 44 This won’t be possible using unlist(). head(unlist(bb, recursive=FALSE)) ##$fa1
## [1] 1
##
## $fa2 ## [1] 1 ## ##$fa3
## [1] 2
##
## $fa4 ## [1] 2 ## ##$ve1
## [1] 31
##
## $ve2 ## [1] 32 To uniform such data to obtain a list with one column only for each list-element, the function asSepList() provides help : bb <- list(fa=gl(2,2), ve=31:33, L2=matrix(21:28,ncol=2), li=list(li1=11:14,li2=data.frame(41:44))) asSepList(bb) ##$fa
## [1] 1 1 2 2
##
## [[2]]
## [1] 11 12 13 14
##
## [[3]]
## [1] NA
##
## $L2_L21 ## [1] 21 22 23 24 ## ##$L2_L22
## [1] 25 26 27 28

### Appending/Combining Lists

Separate lists may be combined using the append() command, which also allows treating simple vectors.

li1 <- list(a=1, b=2, c=3)
li2 <- list(A=11, b=2, C=13)
append(li1, li2)
## $a ## [1] 1 ## ##$b
## [1] 2
##
## $c ## [1] 3 ## ##$A
## [1] 11
##
## $b ## [1] 2 ## ##$C
## [1] 13

However, this way there is no checking if some of the list-elements are present in both lists and thus will appear twice. The function appendNR() allows to checking if some list-elements will appear twice, and thus avoid such duplicate entries.

appendNR(li1, li2)
##  -> appendNR :  adding 2 new names/elements (1 already present)
## $a ## [1] 1 ## ##$b
## [1] 2
##
## $c ## [1] 3 ## ##$A
## [1] 11
##
## $C ## [1] 13 ### rbind On Lists When a matrix (or data.frame) gets split into a list, like in the example using by(), as a reverse-function such lists can get joined using lrbind() in an rbind-like fashion. dat2 <- matrix(11:34, ncol=3, dimnames=list(letters[1:8],colnames=LETTERS[1:3])) lst2 <- by(dat2, rep(1:3,c(3,2,3)), as.matrix) lst2 ## INDICES: 1 ## A B C ## a 11 19 27 ## b 12 20 28 ## c 13 21 29 ## ------------------------------------------------------------ ## INDICES: 2 ## A B C ## d 14 22 30 ## e 15 23 31 ## ------------------------------------------------------------ ## INDICES: 3 ## A B C ## f 16 24 32 ## g 17 25 33 ## h 18 26 34 # join list-elements (back) into single matrix lrbind(lst2) ## A B C ## a 11 19 27 ## b 12 20 28 ## c 13 21 29 ## d 14 22 30 ## e 15 23 31 ## f 16 24 32 ## g 17 25 33 ## h 18 26 34 ### Fuse Content Of List-Elements With Redundant (Duplicated) Names When list-elements have the same name, their content (of named numeric or character vectors) may get fused using fuseCommonListElem() according to the names of the list-elements : val1 <- 10 +1:26 names(val1) <- letters (lst1 <- list(c=val1[3:6], a=val1[1:3], b=val1[2:3] ,a=val1[12], c=val1[13])) ##$c
##  c  d  e  f
## 13 14 15 16
##
## $a ## a b c ## 11 12 13 ## ##$b
##  b  c
## 12 13
##
## $a ## l ## 22 ## ##$c
##  m
## 23
## here the names 'a' and 'c' appear twice :
names(lst1)
## [1] "c" "a" "b" "a" "c"
## now, let's fuse all 'a' and 'c'
fuseCommonListElem(lst1)
## $c ## c d e f m ## 13 14 15 16 23 ## ##$a
##  a  b  c  l
## 11 12 13 22
##
## $b ## b c ## 12 13 ### Filtering Lines And/Or Columns For All List-Elements Of Same Size In a number of cases the information in various list-elements is somehow related. Eg, in S3-objects produced by limma, or data produced using wrProteo several instances of matrix or data.frame refer to data that are related. Some matrixes may conatain abundance data (or weights, etc) while another matrix or data.frame may contain the annotation information related to each line of the abundance data. So if one wants to filter the data, ie remove some lines, this should be done in the same way with all related list-elements. This way one may maintain a conventient 1:1 matching of lines. The function filterLiColDeList() searches if other list-elements have suitable dimensions and will then run the same filtering as in the ‘target’ list-element. In consequence this can be used with the output of wrProteo to remove simultaneously the same lines and/or columns. lst1 <- list(m1=matrix(11:18,ncol=2), m2=matrix(21:30,ncol=2), indR=31:34, m3=matrix(c(21:23,NA,25:27,NA),ncol=2)) filterLiColDeList(lst1, useLines=2:3) ## -> filterLiColDeList : successfully filtered 'm1' and 'm3' from 4 to 2 lines ##$m1
##      [,1] [,2]
## [1,]   12   16
## [2,]   13   17
##
## $m2 ## [,1] [,2] ## [1,] 21 26 ## [2,] 22 27 ## [3,] 23 28 ## [4,] 24 29 ## [5,] 25 30 ## ##$indR
## [1] 31 32 33 34
##
## $m3 ## [,1] [,2] ## [1,] 22 26 ## [2,] 23 27 filterLiColDeList(lst1, useLines="allNA", ref=3) ## -> filterLiColDeList : It appears lst[[ref]] is not matrix (or data.frame) ! Trying to reformat .. ## -> filterLiColDeList : 'useLines' seems empty, nothing to do ... ##$m1
##      [,1] [,2]
## [1,]   11   15
## [2,]   12   16
## [3,]   13   17
## [4,]   14   18
##
## $m2 ## [,1] [,2] ## [1,] 21 26 ## [2,] 22 27 ## [3,] 23 28 ## [4,] 24 29 ## [5,] 25 30 ## ##$indR
##      [,1]
## [1,]   31
## [2,]   32
## [3,]   33
## [4,]   34
##
## $m3 ## [,1] [,2] ## [1,] 21 25 ## [2,] 22 26 ## [3,] 23 27 ## [4,] NA NA ### Replacements In List The function listBatchReplace() works similar to sub() and allows to search & replace exact matches to a character string along all elements of a list. (lst1 <- list(aa=1:4, bb=c("abc","efg","abhh","effge") ,cc=c("abdc","efg","efgh"))) ##$aa
## [1] 1 2 3 4
##
## $bb ## [1] "abc" "efg" "abhh" "effge" ## ##$cc
## [1] "abdc" "efg"  "efgh"
listBatchReplace(lst1,search="efg",repl="EFG",silent=FALSE)
## $aa ## [1] "1" "2" "3" "4" ## ##$bb
## [1] "abc"   "EFG"   "abhh"  "effge"
##
## $cc ## [1] "abdc" "EFG" "efgh" ### Organize Values Into list and Sort By Names Named numeric or character vectors can be organized into lists using listGroupsByNames(), based on their names (only the part before any extensions starting with a point gets considered). Of course, other separators may be defined using the argument sep. ser1 <- 1:7; names(ser1) <- c("AA","BB","AA.1","CC","AA.b","BB.e","A") listGroupsByNames(ser1) ##$AA
##   AA AA.1 AA.b
##    1    3    5
##
## $BB ## BB BB.e ## 2 6 ## ##$CC
## CC
##  4
##
## $A ## A ## 7 If no names are present, the content of the vector itself will be used as name : listGroupsByNames((1:10)/5) ## -> listGroupsByNames : no names found in 'x' !! ##$0
##   0   0   0   0
## 0.2 0.4 0.6 0.8
##
## $1 ## 1 1 1 1 1 ## 1.0 1.2 1.4 1.6 1.8 ## ##$2
## 2
## 2

### Batch-filter List-Elements

In the view of object-oriented programming several methods produce results integrated into lists or S3-objects (eg limma). The function filterList() aims facilitating the filtering of all elements of lists or S3-objects. List-elements with inappropriate number of lines will be ignored.

set.seed(2020); dat1 <- round(runif(80),2)
list1 <- list(m1=matrix(dat1[1:40], ncol=8), m2=matrix(dat1[41:80], ncol=8), other=letters[1:8])
rownames(list1$m1) <- rownames(list1$m2) <- paste0("line",1:5)
# Note: the list-element list1$other has a length different to that of filt. Thus, it won't get filtered. filterList(list1, list1$m1[,1] >0.4)       # filter according to 1st column of $m1 ... ##$m1
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## line1 0.65 0.07 0.76 0.54 0.20 0.17 0.96 0.37
## line3 0.62 0.39 0.83 0.65 0.82 0.75 0.96 0.93
## line4 0.48 0.00 0.42 0.55 0.94 0.45 0.95 0.52
##
## $m2 ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] ## line1 0.99 0.57 0.58 0.00 0.21 0.61 0.61 0.30 ## line3 0.86 0.70 0.90 0.22 0.23 0.58 0.39 0.06 ## line4 0.88 0.80 0.52 0.54 0.42 0.65 0.47 0.67 ## ##$other
## [1] "a" "b" "c" "d" "e" "f" "g" "h"
filterList(list1, list1$m1 >0.4)  ##$m1
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## line1 0.65 0.07 0.76 0.54 0.20 0.17 0.96 0.37
## line3 0.62 0.39 0.83 0.65 0.82 0.75 0.96 0.93
## line4 0.48 0.00 0.42 0.55 0.94 0.45 0.95 0.52
## line5 0.14 0.62 0.41 0.27 0.88 0.56 0.00 0.22
##
## $m2 ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] ## line1 0.99 0.57 0.58 0.00 0.21 0.61 0.61 0.30 ## line3 0.86 0.70 0.90 0.22 0.23 0.58 0.39 0.06 ## line4 0.88 0.80 0.52 0.54 0.42 0.65 0.47 0.67 ## line5 0.62 0.17 0.83 0.49 0.86 0.17 0.53 0.72 ## ##$other
## [1] "a" "b" "c" "d" "e" "f" "g" "h"

### Transform Columns Of Matrix To List Of Vectors

At some occasions it may be useful separate columns of a matrix into separate vectors inside a list. This can be done using matr2list():

(mat1 <- matrix(1:12, ncol=3, dimnames=list(letters[1:4],LETTERS[1:3])))
##   A B  C
## a 1 5  9
## b 2 6 10
## c 3 7 11
## d 4 8 12
str(matr2list(mat1))
## List of 3
##  $A: Named num [1:4] 1 2 3 4 ## ..- attr(*, "names")= chr [1:4] "A.a" "A.b" "A.c" "A.d" ##$ B: Named num [1:4] 5 6 7 8
##   ..- attr(*, "names")= chr [1:4] "B.a" "B.b" "B.c" "B.d"
##  $C: Named num [1:4] 9 10 11 12 ## ..- attr(*, "names")= chr [1:4] "C.a" "C.b" "C.c" "C.d" ## Working With Arrays Let’s get stared with a little toy-array: (arr1 <- array(c(6:4,4:24), dim=c(4,3,2), dimnames=list(c(LETTERS[1:4]), paste("col",1:3,sep=""),c("ch1","ch2")))) ## , , ch1 ## ## col1 col2 col3 ## A 6 5 9 ## B 5 6 10 ## C 4 7 11 ## D 4 8 12 ## ## , , ch2 ## ## col1 col2 col3 ## A 13 17 21 ## B 14 18 22 ## C 15 19 23 ## D 16 20 24 ### CV (Coefficient Of Variance) With Arrays Now we can obtain the CV (coefficient of variance) by splitting along 3rd dimesion (ie this is equivalent to an apply along the 3rd dimension) using arrayCV(): arrayCV(arr1) ## ch1 ch2 ## A 0.3122499 0.2352941 ## B 0.3779645 0.2222222 ## C 0.4788934 0.2105263 ## D 0.5000000 0.2000000 # this is equivalent to cbind(rowCVs(arr1[,,1]), rowCVs(arr1[,,2])) ## [,1] [,2] ## A 0.3122499 0.2352941 ## B 0.3779645 0.2222222 ## C 0.4788934 0.2105263 ## D 0.5000000 0.2000000 Similarly we can split along any other dimension, eg the 2nd dimension : arrayCV(arr1, byDim=2) ## col1 col2 col3 ## A 0.5210260 0.7713892 0.5656854 ## B 0.6698906 0.7071068 0.5303301 ## C 0.8187552 0.6527140 0.4991342 ## D 0.8485281 0.6060915 0.4714045 ### Slice 3-dim Array In List Of Matrixes (Or Arrays) This procedure is similar to (re-)organizing an initial array into clusters, here we split along a user-defined factor/vector. If a clustering-algorithm produces the cluster assignments, this function can be used to organize the input data accordingly using cutArrayInCluLike(). cutArrayInCluLike(arr1, cluOrg=c(2,1,2,1)) ##$2
## , , ch1
##
##   col1 col2 col3
## A    6    5    9
## C    4    7   11
##
## , , ch2
##
##   col1 col2 col3
## A   13   17   21
## C   15   19   23
##
##
## $1 ## , , ch1 ## ## col1 col2 col3 ## B 5 6 10 ## D 4 8 12 ## ## , , ch2 ## ## col1 col2 col3 ## B 14 18 22 ## D 16 20 24 Let’s cut by filtering along the 3rd dimension for all lines where column ‘col2’ is >7, and then display only the content of columns ‘col1’ and ‘col2’ (using filt3dimArr()): filt3dimArr(arr1,displCrit=c("col1","col2"), filtCrit="col2", filtVal=7, filtTy=">") ## [[1]] ## col1 col2 ## 4 8 ## ## [[2]] ## col1 col2 ## A 13 17 ## B 14 18 ## C 15 19 ## D 16 20 ## Working With Redundant Data $$_Semantics_$$ : Please note, that there are two ways of interpreting the term ‘unique’ : • In regular understanding one describes this way an event which occurs only once, and thus does not occur/happen anywhere else. • The command unique() will eliminate redundant entries to obtain a shorter ‘unique’ output vector, ie in the resultant vector all values/content (values) occur only once. However, from the result of unique() you cannot tell any more which ones were not unique initially ! In some applications (eg proteomics) initial identifiers (IDs) may occur multiple times in the data and we frequently need to identify events/values that occur only once, as the first meaning of ‘unique’. This package provides (additional) functions to easily distinguish values occurring just once (ie unique) from those occurring multiple times. Furthermore, there are functions to rename/remove/combine replicated elements, eg correctToUnique() or nonAmbiguousNum(), so that no elements or lines of data get lost. ### Identify What Is Repeated (and Where Repeated Do Occur) ## some text toy data tr <- c("li0","n",NA,NA, rep(c("li2","li3"),2), rep("n",4)) The function table() (from the package base) is very useful get some insights when working with smaller objects, but may be slow to handle very large objects. As mentioned, unique() will make everything unique, and afterwards you won’t know any more who was unique in the first place ! The function duplicated() (also from package base) helps us getting the information who is repeated. table(tr) ## tr ## li0 li2 li3 n ## 1 2 2 5 unique(tr)  ## [1] "li0" "n" NA "li2" "li3" duplicated(tr, fromLast=FALSE) ## [1] FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE aa <- c(11:16,NA,14:12,NA,14) names(aa) <- letters[1:length(aa)] aa ## a b c d e f g h i j k l ## 11 12 13 14 15 16 NA 14 13 12 NA 14 findRepeated() (from this package) will return the position/index (and content/value) of repeated elements. However, the output in form of a list is not very convenient to the human reader. findRepeated(aa)  ##$12
## [1]  2 10
##
## $13 ## [1] 3 9 ## ##$14
## [1]  4  8 12

firstOfRepeated() tells the index of the first instance of repeated elements, which elements you need to make the vector ‘unique’, and which elements get stripped off when making unique. Please note, that NA (no matter if they occure once or more times) are automatically in the part suggested to be removed.

firstOfRepeated(aa)
## $indRepeated ## 12 13 14 ## 2 3 4 ## ##$indUniq
## a b c d e f
## 1 2 3 4 5 6
##
## $indRedund ## g h i j k l ## 7 8 9 10 11 12 aa[firstOfRepeated(aa)$indUniq]          # only unique with their names
##  a  b  c  d  e  f
## 11 12 13 14 15 16
unique(aa)                               # unique() does not return any names !
## [1] 11 12 13 14 15 16 NA

### Correct Vector To Unique (While Maintaining The Original Vector Length)

If necessary, a counter can be added to non-unique entries, thus no individual values get eliminated and the length and order of the resultant object maintains the same using correctToUnique().

This is of importance when assigning rownames to a data.frame : Assigning redundant values/text as rownames of a data.frame will result in an error !

correctToUnique(aa)
##      a      b      c      d      e      f      g      h      i      j      k
##   "11" "12_1" "13_1" "14_1"   "15"   "16" "NA_1" "14_2" "13_2" "12_2" "NA_2"
##      l
## "14_3"
correctToUnique(aa, sep=".", NAenum=FALSE)       # keep NAs (ie without transforming to character)
##      a      b      c      d      e      f      g      h      i      j      k
##   "11" "12.1" "13.1" "14.1"   "15"   "16"     NA "14.2" "13.2" "12.2"     NA
##      l
## "14.3"

You see from the last example above, that this function has an argument for controlling enumerating elements.

### Mark Any Duplicated (ie Ambiguous) Elements by Changing Their Names (and Separate from Unqiue)

First, the truly unique values are reported and then the first occurance of repeated elements is given, NA instances get ignored. This can be done using nonAmbiguousNum() which maintains the length of the initial character vector.

unique(aa)                                    # names are lost
## [1] 11 12 13 14 15 16 NA
nonAmbiguousNum(aa)
##     a     e     f amb_b amb_c
##    11    15    16    12    13
nonAmbiguousNum(aa, uniq=FALSE, asLi=TRUE)    # separate in list unique and repeated 
## $unique ## a e f ## 11 15 16 ## ##$ambig
## amb_b amb_c
##    12    13

### Compare Multiple Vectors And Sort By Number Of Common/Repeated Values/Words

The main aim of the function sortByNRepeated() is allowing to compare multiple vectors for common values/words and providing an output sorted by number of repeats.

Suppose 3 persons are asked which cities they wanted to visit. Then we would like to make a counting of the most frequently cited cities. Here we consider individual choices as equally ranked. By default intra-repeats are eliminated.

cities <- c("Bangkok","London","Paris","Singapore","New York City","Istambul","Delhi","Rome","Dubai")
sortByNRepeated(x=cities[c(1:4)], y=cities[c(3,5:8)])
## $1 ## [1] "Bangkok" "Delhi" "Istambul" "London" ## [5] "New York City" "Rome" "Singapore" ## ##$2
## [1] "Paris"
## or (unlimited) multiple inputs via list
choices1 <- list(Mary=cities[c(1:4)], Olivia=cities[c(3,5:8)], Paul=cities[c(5:3,9,5)])
## Note : Paul cited NYC twice !
table(unlist(choices1))
##
##       Bangkok         Delhi         Dubai      Istambul        London
##             1             1             1             1             1
## New York City         Paris          Rome     Singapore
##             3             3             1             2
sortByNRepeated(choices1)
## $1 ## [1] "Bangkok" "Delhi" "Dubai" "Istambul" "London" "Rome" ## ##$2
## [1] "New York City" "Singapore"
##
## $3 ## [1] "Paris" sortByNRepeated(choices1, filterIntraRep=FALSE) # without correcting multiple citation by Paul ##$1
## [1] "Bangkok"  "Delhi"    "Dubai"    "Istambul" "London"   "Rome"
##
## $2 ## [1] "Singapore" ## ##$3
## [1] "New York City" "Paris"

### Combine Multiple Matrixes Where Some Column-Names Are The Same

Here, it is supposed that you want to join 2 or more matrixes describing different properties of the same collection of individuals (as rows). Common column-names are interpreted that their respective information should be combined (either as average or as sum). This can be done using cbindNR() :

## First we'll make soe toy data :
(ma1 <- matrix(1:6, ncol=3, dimnames=list(1:2,LETTERS[3:1])))
##   C B A
## 1 1 3 5
## 2 2 4 6
(ma2 <- matrix(11:16, ncol=3, dimnames=list(1:2,LETTERS[3:5])))
##    C  D  E
## 1 11 13 15
## 2 12 14 16
## now we can join 2 or more matrixes
cbindNR(ma1, ma2, summarizeAs="mean")       # average of both columns 'C'
##  -> cbindNR :  treating 5 different (types of) columns : C B A D E
##  -> cbindNR :   sorting columns of output
##   A B C  D  E
## 1 5 3 6 13 15
## 2 6 4 7 14 16

### Filter Matrix To Keep Only First Of Repeated Lines

This ressembles to the functioning of unique(), but applies to a user-specified column of the matrix.

(mat1 <- matrix(c(1:6,rep(1:3,1:3)), ncol=2, dimnames=list(letters[1:6],LETTERS[1:2])))
##   A B
## a 1 1
## b 2 2
## c 3 2
## d 4 3
## e 5 3
## f 6 3

The function firstLineOfDat() allows to access/extract the first line of repeated instances.

firstLineOfDat(mat1, refCol=2)
##   A B
## a 1 1
## b 2 2
## d 4 3

This function was rather designed for dealing with character input, it allows concatenating all columns and to remove redundant.

mat2 <- matrix(c("e","n","a","n","z","z","n","z","z","b",
"","n","c","n","","","n","","","z"), ncol=2)
firstOfRepLines(mat2, out="conc")
## [1] "e"  "nn" "ac" "z"  "bz"
# or as index :
firstOfRepLines(mat2)
## [1]  1  2  3  5 10

### Filter To Unique Column-Content Of Matrix, Add Counter And Concatenated Information

(df1 <- data.frame(cbind(xA=letters[1:5], xB=c("h","h","f","e","f"), xC=LETTERS[1:5])))
##   xA xB xC
## 1  a  h  A
## 2  b  h  B
## 3  c  f  C
## 4  d  e  D
## 5  e  f  E

The function nonredDataFrame() offers to include a counter of redundant instances encountered (for 1st column specified) :

nonredDataFrame(df1, useCol=c("xB","xC")) 
##   xA xB xC nSamePep concID
## 1  a  h  A        2   C//E
## 3  c  f  C        2   A//B
## 4  d  e  D        1      D
# without counter or concatenating
df1[which(!duplicated(df1[,2])),]
##   xA xB xC
## 1  a  h  A
## 3  c  f  C
## 4  d  e  D
# or
df1[firstOfRepLines(df1,useCol=2),]
##   xA xB xC
## 1  a  h  A
## 3  c  f  C
## 4  d  e  D

### Get First Of Repeated By Column

mat2 <- cbind(no=as.character(1:20), seq=sample(LETTERS[1:15], 20, repl=TRUE),
ty=sample(c("full","Nter","inter"),20,repl=TRUE), ambig=rep(NA,20), seqNa=1:20)
(mat2uniq <- get1stOfRepeatedByCol(mat2, sortBy="seq", sortSupl="ty"))
##       no   seq ty      ambig  seqNa
##  [1,] "6"  "M" "Nter"  NA     "6"
##  [2,] "11" "C" "inter" NA     "11"
##  [3,] "12" "N" "Nter"  NA     "12"
##  [4,] "17" "J" "full"  NA     "17"
##  [5,] "18" "A" "full"  NA     "18"
##  [6,] "19" "O" "Nter"  NA     "19"
##  [7,] "7"  "B" "Nter"  "TRUE" "_7"
##  [8,] "10" "D" "full"  "TRUE" "_10"
##  [9,] "8"  "E" "full"  "TRUE" "_8"
## [10,] "9"  "F" "full"  "TRUE" "_9"
## [11,] "13" "G" "Nter"  "TRUE" "_13"
## [12,] "3"  "H" "Nter"  "TRUE" "_3"
# the values from column 'seq' are indeed unique
table(mat2uniq[,"seq"])
##
## A B C D E F G H J M N O
## 1 1 1 1 1 1 1 1 1 1 1 1
# This will return all first repeated (may be >1) but without furter sorting
#  along column 'ty' neither marking in comumn 'ambig').
mat2[which(duplicated(mat2[,2],fromLast=FALSE)),]
##      no   seq ty     ambig seqNa
## [1,] "5"  "H" "Nter" NA    "5"
## [2,] "8"  "E" "full" NA    "8"
## [3,] "9"  "F" "full" NA    "9"
## [4,] "13" "G" "Nter" NA    "13"
## [5,] "14" "D" "full" NA    "14"
## [6,] "15" "D" "full" NA    "15"
## [7,] "16" "B" "Nter" NA    "16"
## [8,] "20" "F" "full" NA    "20"

### Transform (ambigous) Matrix To Non-ambiguous Matrix (In Respect To Given Column)

nonAmbiguousMat(mat1,by=2)
##       A B
## 1     1 1
## amb_3 3 2
## amb_6 6 3

Here another example, ambiguous will be marked by an ’_’ :

set.seed(2017); mat3 <- matrix(c(1:100,round(rnorm(200),2)), ncol=3,
dimnames=list(1:100,LETTERS[1:3]));
head(mat3U <- nonAmbiguousMat(mat3, by="B", na="_", uniqO=FALSE), n=15)
##      A     B     C
## 81  81 -2.59 -0.14
## 93  93 -2.02 -0.03
## 7    7 -1.96  0.52
## 4    4 -1.76  0.84
## _74 74 -1.65  0.30
## 55  55 -1.59  1.25
## 52  52 -1.58 -0.24
## 15  15 -1.43 -0.60
## 98  98 -1.34  0.41
## 63  63 -1.33  0.26
## 19  19 -1.13  0.70
## 41  41 -1.06 -0.56
## _56 56 -1.03 -1.07
## 94  94 -0.98 -0.02
## 95  95 -0.97  0.08
head(get1stOfRepeatedByCol(mat3, sortB="B", sortS="B"))
##   A     B     C
## 1 1  1.43  0.02
## 2 2 -0.08  1.38
## 3 3  0.74 -0.07
## 4 4 -1.76  0.84
## 5 5 -0.07 -0.97
## 6 6  0.45 -1.97

### Combine Replicates From List To Matrix

lst2 <- list(aa_1x=matrix(1:12, nrow=4, byrow=TRUE), ab_2x=matrix(24:13, nrow=4, byrow=TRUE))
combineReplFromListToMatr(lst2)
## $a ## 1 ## [1,] 1 ## [2,] 4 ## [3,] 7 ## [4,] 10 ## [5,] 2 ## [6,] 5 ## [7,] 8 ## [8,] 11 ## [9,] 3 ## [10,] 6 ## [11,] 9 ## [12,] 12 ## ##$b
##        2
##  [1,] 24
##  [2,] 21
##  [3,] 18
##  [4,] 15
##  [5,] 23
##  [6,] 20
##  [7,] 17
##  [8,] 14
##  [9,] 22
## [10,] 19
## [11,] 16
## [12,] 13

### Non-redundant Lines Of Matrix

mat4 <- matrix(rep(c(1,1:3,3,1),2), ncol=2, dimnames=list(letters[1:6],LETTERS[1:2]))
nonRedundLines(mat4)
##   A B
## a 1 1
## c 2 2
## d 3 3
## f 1 1

### Filter For Unique Elements /2

# input: c and dd are repeated  :
filtSizeUniq(list(A="a", B=c("b","bb","c"), D=c("dd","d","ddd","c")), filtUn=TRUE, minSi=NULL)
##  -> filtSizeUniq : 2 out of 8 peptides redundant
## $A ## A ## "a" ## ##$B
##  B.1  B.2
##  "b" "bb"
##
## $D ## D.1 D.2 D.3 ## "dd" "d" "ddd" # here a,b,c and dd are repeated : filtSizeUniq(list(A="a", B=c("b","bb","c"), D=c("dd","d","ddd","c")), ref=c(letters[c(1:26,1:3)], "dd","dd","bb","ddd"), filtUn=TRUE, minSi=NULL)  ## -> filtSizeUniq : 8 out of 8 peptides redundant ##$A
## character(0)
##
## $B ## character(0) ## ##$D
## character(0)

### Make Non-redundant Matrix

t3 <- data.frame(ref=rep(11:15,3),tx=letters[1:15],
matrix(round(runif(30,-3,2),1),nc=2), stringsAsFactors=FALSE)

# First we split the data.frame in list
by(t3,t3[,1],function(x) x)
## t3[, 1]: 11
##    ref tx  X1   X2
## 1   11  a 0.4 -1.1
## 6   11  f 0.6  1.0
## 11  11  k 0.1  1.2
## ------------------------------------------------------------
## t3[, 1]: 12
##    ref tx   X1   X2
## 2   12  b  2.0 -0.4
## 7   12  g -0.3  1.8
## 12  12  l -1.4  0.3
## ------------------------------------------------------------
## t3[, 1]: 13
##    ref tx  X1   X2
## 3   13  c 0.8 -1.6
## 8   13  h 0.8 -2.4
## 13  13  m 0.9  1.8
## ------------------------------------------------------------
## t3[, 1]: 14
##    ref tx   X1   X2
## 4   14  d  1.7  0.7
## 9   14  i -1.6 -2.6
## 14  14  n  1.4 -1.1
## ------------------------------------------------------------
## t3[, 1]: 15
##    ref tx   X1   X2
## 5   15  e -0.9  0.4
## 10  15  j -1.7  0.0
## 15  15  o -1.2 -1.8
t(sapply(by(t3,t3[,1],function(x) x), summarizeCols, me="maxAbsOfRef"))
##    [,1] [,2] [,3] [,4]
## 11 11   "a"  0.1  1.2
## 12 12   "b"  -0.3 1.8
## 13 13   "c"  0.8  -2.4
## 14 14   "d"  -1.6 -2.6
## 15 15   "e"  -1.2 -1.8
(xt3 <- makeNRedMatr(t3, summ="mean", iniID="ref"))
##  -> makeNRedMatr : Common summarization method 'mean', run as batch
##  -> makeNRedMatr : Summarize redundant based on col 'ref'  using method(s) : 'mean', 'mean', 'mean' and 'mean' yielding 4 cols
##    ID ref tx         X1         X2 nRedLi
## 11 11  11  a  0.3666667  0.3666667      3
## 12 12  12  b  0.1000000  0.5666667      3
## 13 13  13  c  0.8333333 -0.7333333      3
## 14 14  14  d  0.5000000 -1.0000000      3
## 15 15  15  e -1.2666667 -0.4666667      3
(xt3 <- makeNRedMatr(t3, summ=unlist(list(X1="maxAbsOfRef")), iniID="ref"))
##  -> makeNRedMatr : Summarize redundant based on col 'ref'  using method(s) : 'maxAbsOfRef' and col 'X1' yielding 4 cols
##    ref tx  X1   X2   nRedLi
## 11 11  "a" 0.6  1    3
## 12 12  "b" 2    -0.4 3
## 13 13  "c" 0.9  1.8  3
## 14 14  "d" 1.7  0.7  3
## 15 15  "e" -1.7 0    3

### Combine/Reduce Redundant Lines Based On Specified Column

matr <- matrix(c(letters[1:6],"h","h","f","e",LETTERS[1:5]), ncol=3,
dimnames=list(letters[11:15],c("xA","xB","xC")))
combineRedBasedOnCol(matr, colN="xB")
##   xA    xB  xC
## 2 "a,d" "f" "A,D"
## 3 "b,c" "h" "B,C"
## 1 "e"   "e" "E"
combineRedBasedOnCol(rbind(matr[1,],matr), colN="xB")
##   xA    xB  xC
## 2 "a,d" "f" "A,D"
## 3 "b,c" "h" "B,C"
## 1 "e"   "e" "E"

### Convert Matrix (eg With Redundant) Row-Names To data.frame

x <- 1
dat1 <- matrix(1:10,ncol=2)
rownames(dat1) <- letters[c(1:3,2,5)]
## as.data.frame(dat1)  ...  would result in an error
convMatr2df(dat1)
##     ID X1 X2
## a    a  1  6
## b_1  b  2  7
## c    c  3  8
## b_2  b  4  9
## e    e  5 10
convMatr2df(data.frame(a=as.character((1:3)/2), b=LETTERS[1:3], c=1:3))
##     a b c
## 1 0.5 A 1
## 2 1.0 B 2
## 3 1.5 C 3
tmp <- data.frame(a=as.character((1:3)/2), b=LETTERS[1:3],c=1:3, stringsAsFactors=FALSE)
convMatr2df(tmp)
##     a b c
## 1 0.5 A 1
## 2 1.0 B 2
## 3 1.5 C 3
tmp <- data.frame(a=as.character((1:3)/2), b=1:3, stringsAsFactors=FALSE)
convMatr2df(tmp) 
##     a b
## 1 0.5 1
## 2 1.0 2
## 3 1.5 3

### Find And Combine Points Located Very Close In X/Y Space

set.seed(2013)
datT2 <- matrix(round(rnorm(200)+3,1), ncol=2, dimnames=list(paste("li",1:100,sep=""),
letters[23:24]))
# (mimick) some short and longer names for each line
inf2 <- cbind(sh=paste(rep(letters[1:4],each=26),rep(letters,4),1:(26*4),sep=""),
lo=paste(rep(LETTERS[1:4],each=26), rep(LETTERS,4), 1:(26*4), ",",
rep(letters[sample.int(26)],4), rep(letters[sample.int(26)],4), sep=""))[1:100,]
## We'll use this to test :
head(datT2,n=10)
##        w   x
## li1  2.9 3.7
## li2  3.8 3.3
## li3  2.3 3.3
## li4  4.4 3.1
## li5  4.5 1.8
## li6  0.4 2.4
## li7  3.7 3.3
## li8  3.3 4.0
## li9  5.0 4.1
## li10 1.6 1.1
## let's assign to each pair of x & y values a 'cluster' (column _clu_, the column _combInf_ tells us which lines/indexes are in this cluster)
head(combineOverlapInfo(datT2, disThr=0.03), n=10)
##        w   x       combInf clu isComb
## li1  2.9 3.7 1+16+22+47+91   1   TRUE
## li2  3.8 3.3     2+7+48+54   2   TRUE
## li3  2.3 3.3       3+66+92   3   TRUE
## li4  4.4 3.1             4  52  FALSE
## li5  4.5 1.8             5  53  FALSE
## li6  0.4 2.4             6  54  FALSE
## li7  3.7 3.3     2+7+48+54   2   TRUE
## li8  3.3 4.0         8+100   4   TRUE
## li9  5.0 4.1             9  55  FALSE
## li10 1.6 1.1            10  56  FALSE
## it is also possible to rather display names (eg gene or protein-names) instead of index values
head(combineOverlapInfo(datT2, suplI=inf2[,2], disThr=0.03), n=10)
##        w   x                 combInf clu isComb
## li1  2.9 3.7 AA1+AP16+AV22+BU47+DM91   1   TRUE
## li2  3.8 3.3       AB2+AG7+BV48+CB54   2   TRUE
## li3  2.3 3.3           AC3+CN66+DN92   3   TRUE
## li4  4.4 3.1                  AD4,ww  52  FALSE
## li5  4.5 1.8                  AE5,aj  53  FALSE
## li6  0.4 2.4                  AF6,nl  54  FALSE
## li7  3.7 3.3       AB2+AG7+BV48+CB54   2   TRUE
## li8  3.3 4.0               AH8+DV100   4   TRUE
## li9  5.0 4.1                  AI9,ic  55  FALSE
## li10 1.6 1.1                 AJ10,ee  56  FALSE

### Bin And Summarize Values According To Their Names

dat <- 11:19
names(dat) <- letters[c(6:3,2:4,8,3)]
## Here the names are not unique.
## Thus, the values can be binned by their (non-unique) names and a representative values calculated.

## Let's make a 'datUniq' with the mean of each group of values :
datUniq <- round(tapply(dat, names(dat),mean),1)
## now we propagate the mean values to the full vector
getValuesByUnique(dat, datUniq)
##    f    e    d    c    b    c    d    h    c
## 11.0 12.0 15.0 16.3 15.0 16.3 15.0 18.0 16.3
cbind(ini=dat,firstOfRep=getValuesByUnique(dat, datUniq),
indexUniq=getValuesByUnique(dat, datUniq,asIn=TRUE))
##   ini firstOfRep indexUniq
## f  11       11.0         5
## e  12       12.0         4
## d  13       15.0         3
## c  14       16.3         2
## b  15       15.0         1
## c  16       16.3         2
## d  17       15.0         3
## h  18       18.0         6
## c  19       16.3         2

### Regrouping Simultaneaously by Two Factors

For example, if you wish to create group-labels considering the eye- and hair-color of a small group students (supposed a sort of controlled vocabulary was used), the function combineByEitherFactor() will help. So basically, this is an empiric segmentation-approach for two categorical variables. Please note, that with large data-sets and very disperse data this approach will not provide great results. In the example below we’ll attempt to ‘cluster’ according to columns nn and qq, the resultant cluster number can be found in column grp.

nn <- rep(c("a","e","b","c","d","g","f"),c(3,1,2,2,1,2,1))
qq <- rep(c("m","n","p","o","q"),c(2,1,1,4,4))
nq <- cbind(nn,qq)[c(4,2,9,11,6,10,7,3,5,1,12,8),]
## Here we consider 2 columns 'nn' and 'qq' whe trying to regroup common values
##  (eg value 'a' from column 'nn' and value 'o' from 'qq')
combineByEitherFactor(nq,1,2,nBy=FALSE)
##    nn  qq  grp
## m2 "a" "m" "1"
## q2 "f" "q" "3"
## q1 "d" "q" "3"
## o2 "b" "o" "2"
## p  "e" "p" "4"
## o4 "c" "o" "2"
## q4 "g" "q" "3"
## n  "a" "n" "1"
## m1 "a" "m" "1"
## q3 "g" "q" "3"
## o1 "b" "o" "2"
## o3 "c" "o" "2"

The argument nBy simply allows adding an additional column with the group/cluster-number.

## the same, but including n by group/cluster
combineByEitherFactor(nq,1,2,nBy=TRUE)
##    nn  qq  grp nGrp
## m2 "a" "m" "1" "3"
## q2 "f" "q" "3" "4"
## q1 "d" "q" "3" "4"
## o2 "b" "o" "2" "4"
## p  "e" "p" "4" "1"
## o4 "c" "o" "2" "4"
## q4 "g" "q" "3" "4"
## n  "a" "n" "1" "3"
## m1 "a" "m" "1" "3"
## q3 "g" "q" "3" "4"
## o1 "b" "o" "2" "4"
## o3 "c" "o" "2" "4"
## Not running further iterations works faster, but you may not reach 'convergence' immediately
combineByEitherFactor(nq,1,2,nBy=FALSE)
##    nn  qq  grp
## m2 "a" "m" "1"
## q2 "f" "q" "3"
## q1 "d" "q" "3"
## o2 "b" "o" "2"
## p  "e" "p" "4"
## o4 "c" "o" "2"
## q4 "g" "q" "3"
## n  "a" "n" "1"
## m1 "a" "m" "1"
## q3 "g" "q" "3"
## o1 "b" "o" "2"
## o3 "c" "o" "2"
##  another example
mm <- rep(c("a","b","c","d","e"),c(3,4,2,3,1))
pp <- rep(c("m","n","o","p","q"),c(2,2,2,2,5))
combineByEitherFactor(cbind(mm,pp),1,2, con=FALSE, nBy=TRUE)
##  -> combineByEitherFactor :  did not reach convergence at 2nd pass
##    mm  pp  grp nGrp
## m1 "a" "m" "1" "4"
## m2 "a" "m" "1" "4"
## n1 "a" "n" "1" "4"
## n2 "b" "n" "1" "4"
## o1 "b" "o" "2" "4"
## o2 "b" "o" "2" "4"
## p1 "b" "p" "2" "4"
## p2 "c" "p" "2" "4"
## q1 "c" "q" "3" "5"
## q2 "d" "q" "3" "5"
## q3 "d" "q" "3" "5"
## q4 "d" "q" "3" "5"
## q5 "e" "q" "3" "5"

### Batch Replacing Of Values Or Character-Strings

The function multiCharReplace() facilitates multiple replacements in a vector, matrix or data.frame.

# replace character content
x1 <- c("ab","bc","cd","efg","ghj")
multiCharReplace(x1, cbind(old=c("bc","efg"), new=c("BBCC","EF")))
## [1] "ab"   "BBCC" "cd"   "EF"   "ghj"
# works also on matrix and/or to replace numeric content :
x3 <- matrix(11:16, ncol=2)
multiCharReplace(x3, cbind(12:13,112:113))
##      [,1] [,2]
## [1,]   11   14
## [2,]  112   15
## [3,]  113   16

Sometimes data get imported using different encoding for what should be interpreted as FALSE and TRUE :

# replace and return logical vactor
x2 <- c("High","n/a","High","High","Low")
multiCharReplace(x2,cbind(old=c("n/a","Low","High"), new=c(NA,FALSE,TRUE)), convTo="logical")
## [1]  TRUE    NA  TRUE  TRUE FALSE

### Multi-to-multi Matching Of (Concatenated) Terms

The function allows to split (if necessary, using strsplit()) two vectors and compare each isolated tag (eg identifyer) from the 1st vector/object against each isolated tag from the second vector/object. This runs like a loop of one to many comparisons. The basic output is a list with indexes of which element of the 1st vector/object has matches in the 2nd vector/object. Since this is not convenient to the human reader, tabular output can be created, too.

aa <- c("m","k","j; aa","m; aa; bb; o","n; dd","aa","cc")
bb <- c("aa","dd","aa; bb; q","p; cc")
## result as list of indexes
(bOnA <- multiMatch(aa, bb, method="asIndex"))   # match bb on aa
## $1 ## named integer(0) ## ##$2
## named integer(0)
##
## $3 ## aa aa ## 1 3 ## ##$4
## aa aa bb
##  1  3  3
##
## $5 ## dd ## 2 ## ##$6
## aa aa
##  1  3
##
## $7 ## cc ## 4 ## more convenient to the human reader (bOnA <- multiMatch(aa, bb)) # match bb on aa ## x x.Ind TagBest y.IndBest y.IndAll y.Match y.Adj ## 1 m 1 <NA> NA <NA> <NA> <NA> ## 2 k 2 <NA> NA <NA> <NA> <NA> ## 3 j; aa 3 aa 1 1; 3 aa j; aa ## 4 m; aa; bb; o 4 aa 3 1; 3; 3 aa; bb; q m; aa; bb; o ## 5 n; dd 5 dd 2 2 dd n; dd ## 6 aa 6 aa 1 1; 3 aa aa ## 7 cc 7 cc 4 4 p; cc cc (bOnA <- multiMatch(aa, bb, method="matchedL")) # match bb on aa ##$1
## named integer(0)
##
## $2 ## named integer(0) ## ##$3
## aa aa
##  1  3
##
## $4 ## aa aa bb ## 1 3 3 ## ##$5
## dd
##  2
##
## $6 ## aa aa ## 1 3 ## ##$7
## cc
##  4

### Comparing Global Patterns

In most programming languages it is fairly easy to compare exact content of character vectors or factors with unordered levels. However, sometimes due to semantic issues some people may call a color ‘purple’ while others call it ‘violet’. Thus, without using controled vocabulary the exact terms may vary.

Here, let’s address the case, where no dictionaries are available for substituting equivalent terms. Thus, here we’ll compare 4 vectors of equal length and check if the words/letters used could be substituted to give the first vector. Vectors aa and ab have the same global pattern, ie after repeating a word twice it moves to another word. Vectors ac and ad have different general patterns, either with alternating words or falling back to a word previsously used.

Based and extended on a post on stackoverflow https://stackoverflow.com/questions/71353218/extracting-flexible-general-patterns/ :

aa <- letters[rep(c(3:1,4), each=2)]
ab <- letters[rep(c(5,8:6), each=2)]        # 'same general' pattern to aa
ac <- letters[c(1:2,1:3,3:4,4)]             # NOT 'same general' pattern to any other
ad <- letters[c(6:8,8:6,7:6)]               # NOT 'same general' pattern to any other

The basic pattern can be extracted combining match() and unique():

## get global patterns
cbind(aa= match(aa, unique(aa)),
ab= match(ab, unique(ab)),
ac= match(ac, unique(ac)),
ad= match(ad, unique(ad)) )
##      aa ab ac ad
## [1,]  1  1  1  1
## [2,]  1  1  2  2
## [3,]  2  2  1  3
## [4,]  2  2  2  3
## [5,]  3  3  3  2
## [6,]  3  3  3  1
## [7,]  4  4  4  2
## [8,]  4  4  4  1

Let’s make a data.frame with the annotation toy-data from above. Each line is supposed to represent a sample, and the columns show different aspects of annotation.

bb <- data.frame(ind=1:length(aa), a=aa, b=ab, c=ac, d=ad)

Via the function replicateStructure() is it possible to compare annotation as different columns for equivalent global patterns.

By default, this function excludes all columns not designating any replicates, like the numbers in the first column ($ind). Also it will try to find the column with the median number of levels, when comparing to all other columns. The output is a list with$col inidicating which column(s) may be used, $lev for the correpsonding global pattern,$meth for the method finally used and $allCols for documenting the global pattern in each column (whether it was selected or not). replicateStructure(bb) ##$col
## a
## 2
##
## $lev ## c c b b a a d d ## 1 1 2 2 3 3 4 4 ## ##$meth
## [1] "single median col"

Besides, it is also possible to combine all columns if one cosiders they cotribute complementary substructures of the overal annotation.

replicateStructure(bb, method="combAll")
## $col ## a c d ## 2 4 5 ## ##$lev
## c_a_f c_b_g b_a_h b_b_h a_c_g a_c_f d_d_g d_d_f
##     1     2     3     4     5     6     7     8
##
## $meth ## [1] "comb all col" However, when combining multiple columns it may happen -like in the example above- that finally no more lines remain being considered as replicates. To overcome this problem, it is possible to look for non-orthogonal structures, ie to try excluding columns which (after combining) would suggest no replicates after combining all columns. This can also be found when one column describes the replicate groups and another gives the order of the replicates therein. However, for calling a (standard) statistical test it may be necessary exclude these replicate-numbers to designate the groups of replicates. replicateStructure(bb, method="combNonOrth") ##$col
## a d
## 2 5
##
## $lev ## c_f c_g b_h b_h a_g a_f d_g d_f ## 1 2 3 3 4 5 6 7 ## ##$meth
## [1] "combNonOrth col"

## Search For Similar (Numeric) Values

This section addresses values that are not truly identical but may differ only in the very last digit(s) and thus may be in a pragmatic view get considered and treated as ‘about the same’. The simplest approach would be to round values and then look for identical values. The functions presented here (like checkSimValueInSer()) offer this type of search in a convenient way.

va1 <- c(4:7,7,7,7,7,8:10) +(1:11)/28600
checkSimValueInSer(va1)
##  [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
cbind(va=va1, simil=checkSimValueInSer(va1))
##              va simil
##  [1,]  4.000035     0
##  [2,]  5.000070     0
##  [3,]  6.000105     0
##  [4,]  7.000140     1
##  [5,]  7.000175     1
##  [6,]  7.000210     1
##  [7,]  7.000245     1
##  [8,]  7.000280     1
##  [9,]  8.000315     0
## [10,]  9.000350     0
## [11,] 10.000385     0

### Find Similar Numeric Values Of Two Columns Of A Matrix

The search for similar values may be preformed as absolute distance or as ‘ppm’ (as it is eg usual in proteomics when comparing measured and theoretically expected mass).

aA <- c(11:17); bB <- c(12.001,13.999); cC <- c(16.2,8,9,12.5,15.9,13.5,15.7,14.1,5)
(cloMa <- findCloseMatch(x=aA, y=cC, com="diff", lim=0.5, sor=FALSE))       
## $x2 ## y4 ## 0.5 ## ##$x3
##   y4   y6
## -0.5  0.5
##
## $x4 ## y6 y8 ## -0.5 0.1 ## ##$x6
##   y1   y5   y7
##  0.2 -0.1 -0.3

The result of findCloseMatch() is a list organized by each ‘x’, telling all instances of ‘y’ found within the distance tolerance given by lim. Using closeMatchMatrix() the result obtained above, can be presented in a more convenient format for the human eye.

# all matches (of 2d arg) to/within limit for each of 1st arg ('x'); 'y' ..to 2nd arg = cC
# first let's display only one single closest/best hit
(maAa <- closeMatchMatrix(cloMa, aA, cC, lim=TRUE))  #
##      id.aA aA id.cC   cC disToPred ppmToPred nByGrp isMin nBest
## [1,]     2 12     4 12.5      -0.5  -40000.0      1     1     1
## [2,]     3 13     4 12.5       0.5   40000.0      2     1     2
## [3,]     3 13     6 13.5      -0.5  -37037.0      2     1     2
## [4,]     4 14     8 14.1      -0.1   -7092.2      2     1     1
## [5,]     6 16     5 15.9       0.1    6289.3      3     1     1

Using the argument limitToBest=FALSE we can display all distances within the limits imposed, some values/points may occur multiple times. For example, value number 4 of ‘cC’ (=12.5) or value number 3 of ‘aA’ (=13) now occur multiple times…

(maAa <- closeMatchMatrix(cloMa,aA, cC, lim=FALSE,origN=TRUE))  #
##      id.aA aA id.cC   cC disToPred ppmToPred nByGrp isMin nBest
## [1,]     2 12     4 12.5      -0.5  -40000.0      1     1     1
## [2,]     3 13     4 12.5       0.5   40000.0      2     1     2
## [3,]     3 13     6 13.5      -0.5  -37037.0      2     1     2
## [4,]     4 14     6 13.5       0.5   37037.0      2     0     0
## [5,]     4 14     8 14.1      -0.1   -7092.2      2     1     1
## [6,]     6 16     7 15.7       0.3   19108.0      3     0     0
## [7,]     6 16     5 15.9       0.1    6289.3      3     1     1
## [8,]     6 16     1 16.2      -0.2  -12346.0      3     0     0
(maAa <- closeMatchMatrix(cloMa, cbind(valA=81:87,aA), cbind(valC=91:99,cC), colM=2,
colP=2,lim=FALSE))
##  -> closeMatchMatrix :  reset argument 'origNa' to FALSE since names of 'predMatr' and/or 'measMatr' result of formula and would be too long
##      id.pred valA aA id.meas valC   cC disToPred ppmToPred nByGrp isMin nBest
## [1,]       2   82 12       4   94 12.5      -0.5  -40000.0      1     1     1
## [2,]       3   83 13       4   94 12.5       0.5   40000.0      2     1     2
## [3,]       3   83 13       6   96 13.5      -0.5  -37037.0      2     1     2
## [4,]       4   84 14       6   96 13.5       0.5   37037.0      2     0     0
## [5,]       4   84 14       8   98 14.1      -0.1   -7092.2      2     1     1
## [6,]       6   86 16       7   97 15.7       0.3   19108.0      3     0     0
## [7,]       6   86 16       5   95 15.9       0.1    6289.3      3     1     1
## [8,]       6   86 16       1   91 16.2      -0.2  -12346.0      3     0     0
(maAa <- closeMatchMatrix(cloMa, cbind(aA,valA=81:87), cC, lim=FALSE, deb=TRUE))  #
## .. xxidentToMatr2a
## .. xxidentToMatr2c
##  -> closeMatchMatrix :  reset argument 'origNa' to FALSE since names of 'predMatr' and/or 'measMatr' result of formula and would be too long
## .. xxidentToMatr2d
## .. xxidentToMatr2e
## .. xxidentToMatr2f
##      id.pred aA valA id.meas measMatr disToPred ppmToPred nByGrp isMin nBest
## [1,]       2 12   82       4     12.5      -0.5  -40000.0      1     1     1
## [2,]       3 13   83       4     12.5       0.5   40000.0      2     1     2
## [3,]       3 13   83       6     13.5      -0.5  -37037.0      2     1     2
## [4,]       4 14   84       6     13.5       0.5   37037.0      2     0     0
## [5,]       4 14   84       8     14.1      -0.1   -7092.2      2     1     1
## [6,]       6 16   86       7     15.7       0.3   19108.0      3     0     0
## [7,]       6 16   86       5     15.9       0.1    6289.3      3     1     1
## [8,]       6 16   86       1     16.2      -0.2  -12346.0      3     0     0
a2 <- aA; names(a2) <- letters[1:length(a2)];  c2 <- cC; names(c2) <- letters[10+1:length(c2)]
(cloM2 <- findCloseMatch(x=a2, y=c2, com="diff", lim=0.5, sor=FALSE)) 
## $b ## n ## 0.5 ## ##$c
##    n    p
## -0.5  0.5
##
## $d ## p r ## -0.5 0.1 ## ##$f
##    k    o    q
##  0.2 -0.1 -0.3
(maA2 <- closeMatchMatrix(cloM2, predM=cbind(valA=81:87,a2),
measM=cbind(valC=91:99,c2), colM=2, colP=2, lim=FALSE, asData=TRUE)) 
##  -> closeMatchMatrix :  reset argument 'origNa' to FALSE since names of 'predMatr' and/or 'measMatr' result of formula and would be too long
##     id.pred valA a2 id.meas valC   c2 disToPred ppmToPred nByGrp isMin nBest
## b         b   82 12       n   94 12.5      -0.5  -40000.0      1     1     1
## c_1       c   83 13       n   94 12.5       0.5   40000.0      2     1     2
## c_2       c   83 13       p   96 13.5      -0.5  -37037.0      2     1     2
## d_1       d   84 14       p   96 13.5       0.5   37037.0      2     0     0
## d_2       d   84 14       r   98 14.1      -0.1   -7092.2      2     1     1
## f_1       f   86 16       q   97 15.7       0.3   19108.0      3     0     0
## f_2       f   86 16       o   95 15.9       0.1    6289.3      3     1     1
## f_3       f   86 16       k   91 16.2      -0.2  -12346.0      3     0     0
(maA2 <- closeMatchMatrix(cloM2, cbind(id=names(a2),valA=81:87,a2), cbind(id=names(c2),
valC=91:99,c2), colM=3, colP=3, lim=FALSE, deb=FALSE)) 
##  -> closeMatchMatrix :  reset argument 'origNa' to FALSE since names of 'predMatr' and/or 'measMatr' result of formula and would be too long
##   id.pred valA a2   id.meas valC c2     disToPred             ppmToPred nByGrp
## b "b"     "82" "12" "n"     "94" "12.5" "-0.5"                "-40000"  "1"
## c "c"     "83" "13" "n"     "94" "12.5" "0.5"                 "40000"   "2"
## c "c"     "83" "13" "p"     "96" "13.5" "-0.5"                "-37037"  "2"
## d "d"     "84" "14" "p"     "96" "13.5" "0.5"                 "37037"   "2"
## d "d"     "84" "14" "r"     "98" "14.1" "-0.0999999999999996" "-7092.2" "2"
## f "f"     "86" "16" "q"     "97" "15.7" "0.300000000000001"   "19108"   "3"
## f "f"     "86" "16" "o"     "95" "15.9" "0.0999999999999996"  "6289.3"  "3"
## f "f"     "86" "16" "k"     "91" "16.2" "-0.199999999999999"  "-12346"  "3"
##   isMin nBest
## b "1"   "1"
## c "1"   "2"
## c "1"   "2"
## d "0"   "0"
## d "1"   "1"
## f "0"   "0"
## f "1"   "1"
## f "0"   "0"

### Find Similar Numeric Values From Two Vectors/Matrixes

For comparing two sets of data one may use findSimilarFrom2sets().

aA <- c(11:17); bB <- c(12.001,13.999); cC <- c(16.2,8,9,12.5,12.6,15.9,14.1)
aZ <-  matrix(c(aA,aA+20), ncol=2, dimnames=list(letters[1:length(aA)],c("aaA","aZ")))
cZ <-  matrix(c(cC,cC+20), ncol=2, dimnames=list(letters[1:length(cC)],c("ccC","cZ")))
findCloseMatch(cC,aA,com="diff",lim=0.5,sor=FALSE)
## $x1 ## y6 ## -0.2 ## ##$x4
##   y2   y3
## -0.5  0.5
##
## $x5 ## y3 ## 0.4 ## ##$x6
##  y6
## 0.1
##
## x7 ## y4 ## -0.1 findSimilFrom2sets(aA,cC) ## aA predMatr[, ] cC measMatr disToPred ppmToPred nByGrp isMin nBest ## [1,] 2 12 4 12.5 -0.5 -40000.0 1 1 1 ## [2,] 3 13 4 12.5 0.5 40000.0 2 0 0 ## [3,] 3 13 5 12.6 0.4 31746.0 2 1 1 ## [4,] 4 14 7 14.1 -0.1 -7092.2 1 1 1 ## [5,] 6 16 6 15.9 0.1 6289.3 2 1 1 ## [6,] 6 16 1 16.2 -0.2 -12346.0 2 0 0 findSimilFrom2sets(cC,aA) ## cC predMatr[, ] aA measMatr disToPred ppmToPred nByGrp isMin nBest ## [1,] 1 16.2 6 16 0.2 12500.0 1 1 1 ## [2,] 4 12.5 2 12 0.5 41667.0 2 1 2 ## [3,] 4 12.5 3 13 -0.5 -38462.0 2 1 2 ## [4,] 5 12.6 3 13 -0.4 -30769.0 1 1 1 ## [5,] 6 15.9 6 16 -0.1 -6250.0 1 1 1 ## [6,] 7 14.1 4 14 0.1 7142.9 1 1 1 findSimilFrom2sets(aA,cC,best=FALSE) ## aA predMatr[, ] cC measMatr disToPred ppmToPred nByGrp isMin nBest ## [1,] 2 12 4 12.5 -0.5 -40000.0 1 1 1 ## [2,] 3 13 4 12.5 0.5 40000.0 2 0 0 ## [3,] 3 13 5 12.6 0.4 31746.0 2 1 1 ## [4,] 4 14 7 14.1 -0.1 -7092.2 1 1 1 ## [5,] 6 16 6 15.9 0.1 6289.3 2 1 1 ## [6,] 6 16 1 16.2 -0.2 -12346.0 2 0 0 findSimilFrom2sets(aA,cC,comp="ppm",lim=5e4,deb=TRUE) ## xxfindSimilFrom2sets2 ## .. xxidentToMatr2a ## .. xxidentToMatr2c ## .. xxidentToMatr2d ## .. xxidentToMatr2e ## .. xxidentToMatr2f ## aA predMatr[, ] cC measMatr disToPred ppmToPred nByGrp isMin nBest ## [1,] 2 12 4 12.5 -0.5 -40000.0 1 1 1 ## [2,] 3 13 4 12.5 0.5 40000.0 2 0 0 ## [3,] 3 13 5 12.6 0.4 31746.0 2 1 1 ## [4,] 4 14 7 14.1 -0.1 -7092.2 1 1 1 ## [5,] 6 16 6 15.9 0.1 6289.3 2 1 1 ## [6,] 6 16 1 16.2 -0.2 -12346.0 2 0 0 ## [7,] 7 17 1 16.2 0.8 49383.0 1 1 1 findSimilFrom2sets(aA,cC,comp="ppm",lim=9e4,bestO=FALSE) ## aA predMatr[, ] cC measMatr disToPred ppmToPred nByGrp isMin nBest ## [1,] 2 12 4 12.5 -0.5 -40000.0 2 1 1 ## [2,] 2 12 5 12.6 -0.6 -47619.0 3 0 0 ## [3,] 3 13 4 12.5 0.5 40000.0 2 0 0 ## [4,] 3 13 5 12.6 0.4 31746.0 3 1 1 ## [5,] 3 13 7 14.1 -1.1 -78014.0 3 0 0 ## [6,] 4 14 7 14.1 -0.1 -7092.2 1 1 1 ## [7,] 5 15 7 14.1 0.9 63830.0 3 1 2 ## [8,] 5 15 6 15.9 -0.9 -56604.0 3 1 2 ## [9,] 5 15 1 16.2 -1.2 -74074.0 2 0 0 ## [10,] 6 16 6 15.9 0.1 6289.3 3 0 0 ## [11,] 6 16 1 16.2 -0.2 -12346.0 2 0 0 ## [12,] 7 17 6 15.9 1.1 69182.0 2 1 0 ## [13,] 7 17 1 16.2 0.8 49383.0 2 1 2 # below: find fewer 'best matches' since search window larger (ie more good hits compete !) findSimilFrom2sets(aA,cC,comp="ppm",lim=9e4,bestO=TRUE)  ## aA predMatr[, ] cC measMatr disToPred ppmToPred nByGrp isMin nBest ## [1,] 2 12 4 12.5 -0.5 -40000.0 2 1 1 ## [2,] 3 13 5 12.6 0.4 31746.0 3 1 1 ## [3,] 4 14 7 14.1 -0.1 -7092.2 1 1 1 ## [4,] 5 15 7 14.1 0.9 63830.0 3 1 2 ## [5,] 5 15 6 15.9 -0.9 -56604.0 3 1 2 ## [6,] 7 17 6 15.9 1.1 69182.0 2 1 0 ## [7,] 7 17 1 16.2 0.8 49383.0 2 1 2 ### Fuse Previously Identified Pairs To ‘Clusters’ When you have already identified the closest neighbour of a set of values, you may want to re-organize/fuse such pairs to a given number of total clusters (using fusePairs()). (daPa <- matrix(c(1:5,8,2:6,9), ncol=2)) ## [,1] [,2] ## [1,] 1 2 ## [2,] 2 3 ## [3,] 3 4 ## [4,] 4 5 ## [5,] 5 6 ## [6,] 8 9 fusePairs(daPa, maxFuse=4) ## 1 2 3 4 4 5 6 8 9 ## 1 1 1 1 2 2 2 3 3 ### Eliminate Close (Overlapping) Points (In Bivariate x & y Space) When visualizing larger data-sets in an x&y space one may find many points overlapping when their values are almost the same. The function elimCloseCoord() aims to do reduce a bivariate data-set to ‘non-overlapping’ points, somehow similar to human perception. da1 <- matrix(c(rep(0:4,5),0.01,1.1,2.04,3.07,4.5), ncol=2); da1[,1] <- da1[,1]*99; head(da1) ## [,1] [,2] ## [1,] 0 0 ## [2,] 99 1 ## [3,] 198 2 ## [4,] 297 3 ## [5,] 396 4 ## [6,] 0 0 elimCloseCoord(da1) ## -> elimCloseCoord : reducing 'x' from 15 to 7 lines ## [,1] [,2] ## 1 0 0.0 ## 2 99 1.0 ## 3 198 2.0 ## 4 297 3.0 ## 5 396 4.0 ## 12 99 1.1 ## 15 396 4.5 ### Mode Of (Continuous) Data Looking for the mode is rather easy with counting data, the result of table() will get you there quickly. However, with continuous data the mode may be more tricky to defne and identify. Intuitively most people consider the mode asthe peak of a density estimation (which remains to be defined and estimated). With continuous data most frequent (precise) value may be quite different/distant to the most dense region of data. The function stableMode() presented here has different modes of operation, at this point there is no clear rule which mode may perform most satisfactory in different situations. set.seed(2012); dat <- round(c(rnorm(120,0,1.2), rnorm(80,0.8,0.6), rnorm(25,-0.6,0.05), runif(200)),3) dat <- dat[which(dat > -2 & dat <2)] stableMode(dat) ## -> stableMode : Method='density', length of x =406, 'bandw' has been set to 28 ## 221 ## 0.477 Now we can try to show on a plot : layout(1:2) plot(1:length(dat), sort(dat), type="l", main="Sorted Values", xlab="rank", las=1) abline(h=stableMode(dat, silent=TRUE), lty=2,col=2) legend("topleft",c("stableMode"), text.col=2, col=2, lty=2, lwd=1, seg.len=1.2, cex=0.8, xjust=0, yjust=0.5) plot(density(dat, kernel="gaussian", adjust=0.7), xlab="Value of dat", main="Density Estimate Plot") useCol <- c("red","green","blue","grey55") legend("topleft",c("dens","binning","BBmisc","allModes"), text.col=useCol, col=useCol, lty=2, lwd=1, seg.len=1.2, cex=0.8, xjust=0, yjust=0.5) abline(v=stableMode(dat, method="dens", silent=TRUE), lty=2, col="red", lwd=2) abline(v=stableMode(dat, method="binning", silent=TRUE), lty=2, col="green") abline(v=stableMode(dat, method="BBmisc", silent=TRUE), lty=2, col="blue")  ## Loading required namespace: BBmisc abline(v=stableMode(dat, method="allModes"), lty=2, col="grey55")  Please note, that plotting data modelled via a Kernell function (as above) also relies on strong hypothesis which may not be well justified in a number of cases ! For this reason, the sorted values were plotted, too. As you can see from this example above, looking for the most frequent exact value may not be a perfect choice for continous data. In this example the method ‘allModes’ (ie the multiple instances of most frequent exact values) gave partially usable results (dashed grey lines), due to the rounding to 3 digits. As you can see in the example above, the method ‘allModes’ may give multiple ties ! More rounding will make to data more discrete and ultimately ressemble cunting data. However, with rounding some of the finer resolution/details will get lost. ### Most Frequently Occuring Value (traditional mode) The function stableMode() can also be used to locate the most frequently occuring exact value of numeric or character vectors. As we just saw at the end of the previous example, the argument method=“allModes” allows finding all ties (if present). set.seed(2021) x <- sample(letters, 50000, replace=TRUE) stableMode(dat, method="mode") ## [1] 0.173 stableMode(dat, method="allModes") ## [1] 0.173 0.629 0.676 ### Trimming Redundant Text Automatic annotation has the tendency to concatenate many parameters into a single names. The function trimRedundText() was designed to allow trimming redundant text from left and/or right side of a character-vector (when the same portion of text appears in each element). However, as in some cases (like the first element of the example below) nothing would remain, it is possible to define a minimum width for the remaining/resulting text. txt1 <- c("abcd","abcde","abcdefg","abcdE",NA,"abcdEF") trimRedundText(txt1) ## [1] "abcd" "abcde" "abcdefg" "abcdE" NA "abcdEF" ### Extract Common Part Of Text The original idea was to do something resembling the inverse process of trimming redundant text (example above), but this time to discard the variable text. In the end this is not as trivial when ‘common’ or ‘redundant’ text is not at the beginning or end of a chain of characters. In particular with very large text this is an active field of research (eg for sequence alignment). The function presented here is a very light-weight solution designed for smaller and simple settings, like inspecting column-names. Furthermore, the function keepCommonText() only reports the first (longest) hit. So, when there are multiple conserved ‘words’ of equal length, only the first of them will be identified. When setting the argument ‘hiResol=FALSE’ this function has an option to decrease the resulution of searching, which in turn increases the speed, howevere, at cost of missing the optimal solution. In this case the resultant chain of characters should be inspected if it can be further extended/optimized. With terminal common text : txt1 <- c("abcd","abcde","abcdefg","abcdE",NA,"abcdEF") trimRedundText(txt1, side="left") # remove redundant  ## [1] "abcd" "abcde" "abcdefg" "abcdE" NA "abcdEF" keepCommonText(txt1, side="terminal") # keep redundant ## [1] "abc" keepCommonText(txt1, side="center") # computationally easier  ## [1] "ab" With internal coomon text: txt2 <- c("abcd_abc_kjh", "bcd_abc123", "cd_abc_po") keepCommonText(txt2, side="center")  ## [1] "cd_abc" ## Working With Regressions ### Best Starting Point For Linear Regressions In many types of measurments the very low level measures are delicate. Especially when the readout starts with a baseline signal before increasing amounts of the analyte start producing a linear relationship. In such cases some of the very lowest levels of the analyte are masked by the (random) baseline signal. The function linModelSelect() presented here allows omitting some of the lowest analyte measures to focus on the linear part of the dose-response relationship. li1 <- rep(c(4,3,3:6),each=3) + round(runif(18)/5,2) names(li1) <- paste0(rep(letters[1:5], each=3), rep(1:3,6)) li2 <- rep(c(6,3:7), each=3) + round(runif(18)/5, 2) dat2 <- rbind(P1=li1, P2=li2) exp2 <- rep(c(11:16), each=3) exp4 <- rep(c(3,10,30,100,300,1000), each=3) ## Check & plot for linear model linModelSelect("P1", dat2, expect=exp2) ## -> linModelSelect : best slope pVal starting at level no 3 ##coef
##             Estimate Std. Error   t value     Pr(>|t|)
## (Intercept)   -9.523 0.24178213 -39.38670 2.659588e-12
## conc           0.974 0.01662528  58.58547 5.098121e-14
##
## $name ## [1] "P1" ## ##$startLev
## [1] 3
linModelSelect("P2", dat2, expect=exp2)
##  -> linModelSelect :  best slope pVal starting at level no 2

## $coef ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) -9.028667 0.099481734 -90.75703 1.320255e-19 ## conc 1.006000 0.007069859 142.29421 3.838234e-22 ## ##$name
## [1] "P2"
##
## $startLev ## [1] 2 ### High Throughput Testing For Linear Regressions Once we have run multiple linear regressions on differt parts of the data we might wat to compare them in a single plot. Below, we construct 10 series of data that get modeled the same way, ideally one would obtain a slope close to 1.0. We still allow omitting some starting points, if the resulting model would fit better. set.seed(2020) x1 <- matrix(rep(c(2,2:5),each=20) + runif(100) +rep(c(0,0.5,2:3,5),20), byrow=FALSE, ncol=10, dimnames=list(LETTERS[1:10],NULL)) ## just the 1st regression : summary(lm(b~a, data=data.frame(b=x1[,1], a=rep(1:5,each=2)))) ## ## Call: ## lm(formula = b ~ a, data = data.frame(b = x1[, 1], a = rep(1:5, ## each = 2))) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.3811 -0.6719 -0.5001 1.3683 2.6876 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.7850 1.3668 2.038 0.0759 . ## a 0.5545 0.4121 1.346 0.2153 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 1.843 on 8 degrees of freedom ## Multiple R-squared: 0.1846, Adjusted R-squared: 0.08263 ## F-statistic: 1.811 on 1 and 8 DF, p-value: 0.2153 ## all regressions x1.lmSum <- t(sapply(lapply(rownames(x1), linModelSelect, dat=x1, expect=rep(1:5,each=2), silent=TRUE, plotGraph=FALSE), function(x) c(x$coef[2,c(4,1)], startFr=x$startLev))) x1.lmSum <- cbind(x1.lmSum, medQuantity=apply(x1,1,median)) x1.lmSum[,1] <- log10(x1.lmSum[,1]) head(x1.lmSum) ## Pr(>|t|) Estimate startFr medQuantity ## A -4.298797 0.7837628 1 3.781966 ## B -4.828403 1.1542815 2 3.756802 ## C -5.873269 0.6638477 1 5.883383 ## D -6.518075 0.7793624 1 6.703049 ## E -5.288792 0.8599269 1 8.725195 ## F -5.031322 0.9901120 2 3.286851 Now we can try to plot : wrGraphOK <- requireNamespace("wrGraph", quietly=TRUE) # check if package is available if(wrGraphOK) wrGraph::plotW2Leg(x1.lmSum, useCol=c("Pr(>|t|)","Estimate","medQuantity","startFr"), legendloc="topleft", txtLegend="start at") ## Combinatorics Issues ### All Pairwise Ratios ratioAllComb() calculates all possible pairwise ratios between all individual calues of x and y. set.seed(2014); ra1 <- c(rnorm(9,2,1), runif(8,1,2)) Let’s assume there are 2 parts of ‘x’ for which we would like to know the representative ratio : The ratio of medians does not well reflect the typical ratio (if each element has the same chance to be picked). median(ra1[1:9])/median(ra1[10:17]) ## [1] 1.327086 Instead, we’ll build all possible ratios and summarize then. summary( ratioAllComb(ra1[1:9], ra1[10:17])) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.359 1.142 1.274 1.290 1.506 2.777 boxplot(list(norm=ra1[1:9], unif=ra1[10:17], rat=ratioAllComb(ra1[1:9],ra1[10:17]))) ### Count Frequency Of Terms Combined From Different Drawings (combineAsN) The main idea of this function is to count frequency of terms when combining different drawings. Suppose, you are asking students for their prefered hobbies. Now, you want to know how many terms will occur in common in groups of 3 students. In the example below, simple letters are shown instead of names of hobbies … In the simplest way of using combineAsN() does something similar to table : Here we’re looking at the full combinatorics of making groups of nCombin students and let’s count the frequency of terms found 3 times identical, 2 times or only once (ie not cited by the others). In case multiple groups of nCombin students can be formed, the average of the counts, standard error of the mean (sem), 95% confidence interval (CI) and sd aregiven to resume the results. tm1 <- list(a1=LETTERS[1:7], a2=LETTERS[3:9], a3=LETTERS[6:10], a4=LETTERS[8:12]) combineAsN(tm1, nCombin=3, lev=gl(1,4))[,1,] ## n sem CI sd ## sing 5 0.8164966 2.598457 1.632993 ## doub 5 0.8164966 2.598457 1.632993 ## trip 1 0.5773503 1.837386 1.154701 ## min2 10 1.1547005 3.674772 2.309401 ## any 11 0.5773503 1.837386 1.154701 One may imagine that different locations/coties/countries will give different results. Thus, we’ll declare the different origins/location using the lev argument. Now, this function focusses (by default) on combinations of students from nCombin different origins/location and counts how many hobbies were mentioned as all different (‘sing’, ie number of hobbies only one student mentioned), single repeat (‘doub’) or three times repeated (‘trip’), plus minumum twice or ‘any’ (ie number of hobies citied no matter how many repeats). The output is an array, the 3rd dimension contains the counts, fllowed by sem, CI and sd. ## different levels/groups in list-elements tm4 <- list(a1=LETTERS[1:15], a2=LETTERS[3:16], a3=LETTERS[6:17], a4=LETTERS[8:19], b1=LETTERS[5:19], b2=LETTERS[7:20], b3=LETTERS[11:24], b4=LETTERS[13:25], c1=LETTERS[17:26], d1=LETTERS[4:12], d2=LETTERS[5:11], d3=LETTERS[6:12], e1=LETTERS[7:10]) te4 <- combineAsN(tm4, nCombin=4, lev=substr(names(tm4),1,1)) ## -> combineAsN : argument 'nCombin' combined to 'remDouble'=TRUE is too high for given data, re-setting to 4 str(te4) ## num [1:5, 1:7, 1:4] 4 4 3 8 19 ... ## - attr(*, "dimnames")=List of 3 ## ..$ : chr [1:5] "sing" "doub" "trip" "min2" ...
##   ..$: chr [1:7] "a_a_a_a" "a_b_c_d" "a_b_c_e" "a_b_d_e" ... ## ..$ : chr [1:4] "n" "sem" "CI" "sd"
te4[,,1]           # the counts part only
##      a_a_a_a   a_b_c_d a_b_c_e   a_b_d_e   a_c_d_e b_b_b_b   b_c_d_e
## sing       4  6.562500  7.5000  7.437500 15.333333       3 10.333333
## doub       4 12.583333 12.5625  6.708333  4.166667       8  9.666667
## trip       3  4.395833  2.8750  3.520833  3.750000       3  2.000000
## min2       8 19.145833 20.0625 14.145833 19.500000      11 20.000000
## any       19 23.541667 22.9375 19.541667 23.250000      21 22.000000

## Import/Export

Some software do produce a series of csv files, where a large experiment/data-set get recorded as multiple files. The function readCsvBatch() was designed for reading multiple csv files of exactly the same layout and to join their content. As output a list with the content of each file can be produced (one matrix per file), or the data may be fused into an array, as shown below.

path1 <- system.file("extdata", package="wrMisc")
fiNa <-  c("pl01_1.csv","pl01_2.csv","pl02_1.csv","pl02_2.csv")
str(datAll)
##  num [1:96, 1:4, 1] 158808 174272 183176 175752 49272 ...
##  - attr(*, "dimnames")=List of 3
##   ..$: chr [1:96] "A01" "B01" "C01" "D01" ... ## ..$ : chr [1:4] "1_1.csv" "1_2.csv" "2_1.csv" "2_2.csv"
##   ..$: chr "StainA" When setting the first argument fileNames to NULL, you can read all files of a given path. ## batch reading of all csv files in specified path : datAll2 <- readCsvBatch(fileNames=NULL, path=path1, silent=TRUE) str(datAll2) ## num [1:96, 1:4, 1] 158808 174272 183176 175752 49272 ... ## - attr(*, "dimnames")=List of 3 ## ..$ : chr [1:96] "A01" "B01" "C01" "D01" ...
##   ..$: chr [1:4] "1_1.csv" "1_2.csv" "2_1.csv" "2_2.csv" ## ..$ : chr "StainA"

The function readTabulatedBatch() allows fast batch reading of tabulated files. All files specified (or all files from a given directory) will be read into separate data.frames of a list. Default options are US-style comma, automatic testing for head in case the package data.table is available (otheriwse : no header). Furthermore it is possible to design a given (numeric) column and directly filter for all lines passing a given threshold, allowing to get smaller objects.

path1 <- system.file("extdata", package="wrMisc")
fiNa <-  c("a1.txt","a2.txt")
str(allTxt)
## List of 2
##  $a1.txt:'data.frame': 33 obs. of 3 variables: ## ..$ V1: int [1:33] 3697 3626 732 388503 10747 1564 3699 256394 345 3950 ...
##   ..$V2: num [1:33] 4 6.24 6.63 6.71 8 ... ## ..$ V3: num [1:33] 0.621 0.507 0.575 0.502 0.525 ...
##  $a2.txt:'data.frame': 35 obs. of 3 variables: ## ..$ V1: int [1:35] 6414 57381 8404 10580 79611 4739 10252 221395 4256 4811 ...
##   ..$V2: num [1:35] 1.73 5.83 6.71 7.48 9.49 ... ## ..$ V3: num [1:35] 0.412 0.407 0.391 0.368 0.348 ...

Sometimes were may get confronted with data which look like ‘incomplete’ tables. In such cases some rows do not contain as many elements/columns as other columns. Files with this type of data pose a problem for read.table() (from the utils package). In some cases using the argument fill=TRUE may allow to overcome this problem. The function readVarColumns() (from this package) was designed to provide better help in such cases. Basically each line is read and parsed separately, the user should check/decide on the separator to be used.

The example below lists people’s names in different locations, some locations have more persons … Sometimes exporting such data will generate shorter lines in locations with fewer elements (here ‘London’) and no additional separators will get added (to mark all empty fields) towards the end. The function readVarColumns() (from this package) provides help to read such data, if the content (and separators) of the last columns are missing.

path1 <- system.file("extdata", package="wrMisc")
fiNa <- "Names1.tsv"
datAll <- readVarColumns(fiName=file.path(path1,fiNa), sep="\t")
##  -> readVarColumns :  setting 'refCo' to 'Location'
str(datAll)
##  chr [1:2, 1:5] "Paris" "London" "Caroline" "James" "Marie" "Stella" ...
##  - attr(*, "dimnames")=List of 2
##   ..$: chr [1:2] "Paris" "London" ## ..$ : chr [1:5] "Location" "Names" "Names_2" "Names_3" ...

### Converting Url For Reading Tabulated Data From GitHub

GitHub allows sharing code and (to a lower degree) data. In order to properly read tabulated (txt, tsv or csv) data directly from a given url, the user should switch to the ‘Raw’ view. The function gitDataUrl() allows to conventiently switch any url (on git) to the format from ‘Raw view’, suitable for directly reading the data using read.delim() , read.table() or read.csv() etc …).

## An example url with tabulated data :
gitDataUrl(url1)
## [1] "https://raw.githubusercontent.com/bigbio/proteomics-metadata-standard/master/annotated-projects/PXD001819/PXD001819.sdrf.tsv"
dataPxd <- try(read.delim(gitDataUrl(url1), sep='\t', header=TRUE))
str(dataPxd)
## 'data.frame':    27 obs. of  24 variables:
##  $source.name : chr "Sample 1" "Sample 1" "Sample 1" "Sample 2" ... ##$ characteristics.organism.            : chr  "Saccharomyces cerevisiae" "Saccharomyces cerevisiae" "Saccharomyces cerevisiae" "Saccharomyces cerevisiae" ...
##  $characteristics.organism.part. : chr "not available" "not available" "not available" "not available" ... ##$ characteristics.disease.             : chr  "not available" "not available" "not available" "not available" ...
##  $characteristics.cell.type. : chr "not applicable" "not applicable" "not applicable" "not applicable" ... ##$ characteristics.mass.                : chr  "2 mg" "2 mg" "2 mg" "2 mg" ...
##  $characteristics.spiked.compound. : chr "CT=mixture;QY=12500 amol;CN=UPS1;CV=Standards Research Group" "CT=mixture;QY=12500 amol;CN=UPS1;CV=Standards Research Group" "CT=mixture;QY=12500 amol;CN=UPS1;CV=Standards Research Group" "CT=mixture;QY=125 amol;CN=UPS1;CV=Standards Research Group" ... ##$ characteristics.biological.replicate.: int  1 1 1 1 1 1 1 1 1 1 ...
##  $material.type : chr "lysate" "lysate" "lysate" "lysate" ... ##$ assay.name                           : chr  "run 1" "run 2" "run 3" "run 4" ...
##  $technology.type : chr "proteomic profiling by mass spectrometry" "proteomic profiling by mass spectrometry" "proteomic profiling by mass spectrometry" "proteomic profiling by mass spectrometry" ... ##$ comment.label.                       : chr  "AC=MS:1002038;NT=label free sample" "AC=MS:1002038;NT=label free sample" "AC=MS:1002038;NT=label free sample" "AC=MS:1002038;NT=label free sample" ...
##  $comment.instrument. : chr "AC=MS:1001742;NT=LTQ Orbitrap Velos" "AC=MS:1001742;NT=LTQ Orbitrap Velos" "AC=MS:1001742;NT=LTQ Orbitrap Velos" "AC=MS:1001742;NT=LTQ Orbitrap Velos" ... ##$ comment.precursor.mass.tolerance.    : chr  "5 ppm" "5 ppm" "5 ppm" "5 ppm" ...
##  $comment.fragment.mass.tolerance. : chr "0.8 Da" "0.8 Da" "0.8 Da" "0.8 Da" ... ##$ comment.cleavage.agent.details.      : chr  "NT=trypsin/P;AC=MS:1001313" "NT=trypsin/P;AC=MS:1001313" "NT=trypsin/P;AC=MS:1001313" "NT=trypsin/P;AC=MS:1001313" ...
##  $comment.modification.parameters. : chr "NT=Carbamidomethyl;TA=C;MT=fixed;AC=UNIMOD:4" "NT=Carbamidomethyl;TA=C;MT=fixed;AC=UNIMOD:4" "NT=Carbamidomethyl;TA=C;MT=fixed;AC=UNIMOD:4" "NT=Carbamidomethyl;TA=C;MT=fixed;AC=UNIMOD:4" ... ##$ comment.modification.parameters..1   : chr  "NT=Oxidation;MT=variable;TA=M;AC=UNIMOD:35" "NT=Oxidation;MT=variable;TA=M;AC=UNIMOD:35" "NT=Oxidation;MT=variable;TA=M;AC=UNIMOD:35" "NT=Oxidation;MT=variable;TA=M;AC=UNIMOD:35" ...
##  $comment.modification.parameters..2 : chr "NT=Acetyl;AC=UNIMOD:67;PP=Protein N-term;MT=variable" "NT=Acetyl;AC=UNIMOD:67;PP=Protein N-term;MT=variable" "NT=Acetyl;AC=UNIMOD:67;PP=Protein N-term;MT=variable" "NT=Acetyl;AC=UNIMOD:67;PP=Protein N-term;MT=variable" ... ##$ comment.technical.replicate.         : int  1 2 3 1 2 3 1 2 3 1 ...
##  $comment.fraction.identifier. : int 1 1 1 1 1 1 1 1 1 1 ... ##$ comment.file.uri.                    : chr  "https://ftp.ebi.ac.uk/pride-archive/2015/12/PXD001819/UPS1_12500amol_R1.raw" "https://ftp.ebi.ac.uk/pride-archive/2015/12/PXD001819/UPS1_12500amol_R2.raw" "https://ftp.ebi.ac.uk/pride-archive/2015/12/PXD001819/UPS1_12500amol_R3.raw" "https://ftp.ebi.ac.uk/pride-archive/2015/12/PXD001819/UPS1_125amol_R1.raw" ...
##  $comment.data.file. : chr "UPS1_12500amol_R1.raw" "UPS1_12500amol_R2.raw" "UPS1_12500amol_R3.raw" "UPS1_125amol_R1.raw" ... ##$ factor.value.spiked.compound.        : chr  "CT=mixture;QY=12500 amol;CN=UPS1;CV=Standards Research Group" "CT=mixture;QY=12500 amol;CN=UPS1;CV=Standards Research Group" "CT=mixture;QY=12500 amol;CN=UPS1;CV=Standards Research Group" "CT=mixture;QY=125 amol;CN=UPS1;CV=Standards Research Group" ...

## Normalization

The main reason of normalization is to remove variability in the data which is not directly linked to the (original/biological) concept of a given experiment. High throughput data from real world measurements may easily contain various deformations due to technical reasons, eg slight temperature variations, electromagnetic interference, instability of reagents etc. In particular, transferring constant amounts of liquids/reagents in highly repeated steps over large experiments is often also very challenging, small variations of the amounts of liquid (or similar) are typically addressed by normalization. However, applying aggressive normalization to the data also brings considerable risk of starting to loose some of the effects one intended to study. At some point it may rather be better to eliminate a few samples or branches of an experiment to avoid too invasive intervention. This shows that quality control can be tightly linked to decisions about data-normalization. In conclusion, normalization may be far more challenging than simply running some algorithms..

In general, the use has to assume/define some hypothesis to justify intervention. Sometimes specific elements of an experiment are known to be not affected and can therefore be used to normalize the rest. Eg, if you observe growth of trees in a forest, big blocks of rock on the floor are assumed no to change their location. So one could use them as alignment-marks to superpose pictures taken at slightly different positions.

The hypothesis of no global changes is very common : During the course of many biological experiments (eg change of nutrient) one assumes that only a small portion of the elements measured (eg the abundance of all different gene-products) do change, since many processes of a living cell like growth, replication and interaction with neighbour-cells are assumed not to be affected. So, if one assumes that there are no global changes one normalizes the input-data in a way that the average or median across each experiment will give the same value. In analogy, if one takes photographs on a partially cloudy day, most cameras will adjust light settings (sun r clouds) so that global luminosity stays the same. However, if too many of the measured elements are affected, this normalization approach will lead to (additional) loss of information.

It is essential to understand the type of deformation(s) data may suffer from in order to choose the appropriate approacges for normalization. Of course, graphical representations (PCA, MA-plots, etc) are extremely important to identifying abnormalities and potential problems. The package wrGraph offers also complementary options useful in the context of normalization. Again, graphical representation(s) of the data help to visualize how different normalization procedures affect outcomes.

Before jumping into normalization it may be quite useful to filter the data first. The overall idea is, that most high-throughput experiments do produce some non-meaningful data (artefacts) and it may be wise to remove such ‘bad’ data first, as they may effect normalization (in particular extreme values). A special case of problematic data concerns NA-values.

### Filter Lines Of Matrix To Reduce Content Of NAs

Frequent NA-values may represent another potential issue. With NA-values there is no general optimal advice. To get started, you should try to investigate how and why NA-values occurred to check if there is a special ‘meaning’ to them. For example, on some measurement systems values below detection limit may be simply reported as NAs. If the lines of your data represent different features quantified (eg proteins), than lines with mostly NA-values represent features that may not be well exploited anyway. Therefore many times one tries to filter away lines of ‘bad’ data. Of course, if there is a column (sample) with an extremely high content of NAs, one should also investigate what might be particular with this column (sample), to see if one might be better of to eliminate the entire column.

Please note, that imputing NA-values represents another option instead of filtering and removing, multiple other packages address this in detail, too. All decisions of which approach to use should be data-driven.

#### Filter For Each Group Of Columns For Sufficient Data As Non-NA

filter for each group of columns for sufficient data as non-NA The function presenceGrpFilt() allows to

dat1 <- matrix(1:56,ncol=7)
dat1[c(2,3,4,5,6,10,12,18,19,20,22,23,26,27,28,30,31,34,38,39,50,54)] <- NA
grp1 <- gl(3,3)[-(3:4)]
dat1
##      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,]    1    9   17   25   33   41   49
## [2,]   NA   NA   NA   NA   NA   42   NA
## [3,]   NA   11   NA   NA   35   43   51
## [4,]   NA   NA   NA   NA   36   44   52
## [5,]   NA   13   21   29   37   45   53
## [6,]   NA   14   NA   NA   NA   46   NA
## [7,]    7   15   NA   NA   NA   47   55
## [8,]    8   16   24   32   40   48   56
## now let's filter
presenceGrpFilt(dat1, gr=grp1, presThr=0.75)  # stringent
##          1     2     3
## [1,]  TRUE  TRUE  TRUE
## [2,] FALSE FALSE FALSE
## [3,] FALSE FALSE  TRUE
## [4,] FALSE FALSE  TRUE
## [5,] FALSE  TRUE  TRUE
## [6,] FALSE FALSE FALSE
## [7,]  TRUE FALSE FALSE
## [8,]  TRUE  TRUE  TRUE
presenceGrpFilt(dat1, gr=grp1, presThr=0.25)  # less stringent
##          1     2    3
## [1,]  TRUE  TRUE TRUE
## [2,] FALSE FALSE TRUE
## [3,]  TRUE FALSE TRUE
## [4,] FALSE FALSE TRUE
## [5,]  TRUE  TRUE TRUE
## [6,]  TRUE FALSE TRUE
## [7,]  TRUE FALSE TRUE
## [8,]  TRUE  TRUE TRUE

#### Filter As Separate Pairwise Groups Of Samples

If you want to use your data in a pair-wise view (like running t-tests on each line) the function presenceFilt() allows to eliminate lines containing too many NA-values for each pair-wise combination of the groups/levles.

presenceFilt(dat1, gr=grp1, maxGr=1, ratM=0.1)
##  -> presenceFilt :  correcting 'maxGrpMiss' for group(s) 1, 2 and 3  due to ratMaxNA=0.1
##        1-2   1-3   2-3
## [1,]  TRUE  TRUE  TRUE
## [2,] FALSE FALSE FALSE
## [3,] FALSE  TRUE  TRUE
## [4,] FALSE  TRUE  TRUE
## [5,]  TRUE  TRUE  TRUE
## [6,] FALSE FALSE FALSE
## [7,]  TRUE  TRUE FALSE
## [8,]  TRUE  TRUE  TRUE
presenceFilt(dat1, gr=grp1, maxGr=2, rat=0.5)
##  -> presenceFilt :  correcting 'maxGrpMiss' for group(s) 1 and 2  due to ratMaxNA=0.5
##        1-2  1-3  2-3
## [1,]  TRUE TRUE TRUE
## [2,] FALSE TRUE TRUE
## [3,]  TRUE TRUE TRUE
## [4,] FALSE TRUE TRUE
## [5,]  TRUE TRUE TRUE
## [6,]  TRUE TRUE TRUE
## [7,]  TRUE TRUE TRUE
## [8,]  TRUE TRUE TRUE

#### Cleaning Replicates

This procedures aims to remove (by setting to as NA) the most extreme of noisy replicates. Thus, it is assumed that all columns of the input matrix (or data.frame) are replicates of the other columns. The nOutl most distant points are identified and will be set to NA.

(mat3 <- matrix(c(19,20,30,40, 18,19,28,39, 16,14,35,41, 17,20,30,40), ncol=4))
##      [,1] [,2] [,3] [,4]
## [1,]   19   18   16   17
## [2,]   20   19   14   20
## [3,]   30   28   35   30
## [4,]   40   39   41   40
cleanReplicates(mat3, nOutl=1)
##  -> cleanReplicates :  rownames of 'x' either NULL or not unique, replacing by row-numbers
##  -> cleanReplicates : removing 1 entries in lines 2
##   [,1] [,2] [,3] [,4]
## 1   19   18   16   17
## 2   20   19   NA   20
## 3   30   28   35   30
## 4   40   39   41   40
cleanReplicates(mat3, nOutl=3)
##  -> cleanReplicates :  rownames of 'x' either NULL or not unique, replacing by row-numbers
##  -> cleanReplicates : removing 3 entries in lines 1,2,3
##   [,1] [,2] [,3] [,4]
## 1   NA   18   16   17
## 2   20   19   NA   20
## 3   30   28   NA   30
## 4   40   39   41   40

### The Function normalizeThis()

In biological high-throughput data columns typically represent different samples, which may be organized as replicates. During high-throughput experiments thousands of (independent) elements are measured (eg abundance of gene-products), they are represented by rows. As real-world experiments are not always as perfect as we may think, small changes in the signal measured may easily happen. Thus, the aim of normalizing is to remove or reduce any trace/variability in the data not related to the original experiement but due to imperfections during detection.

Note, that some experiments may produce a considerable amount of missing data (NAs) which require special attention (dedicated developments exist in other R-packages eg in wrProteo). My general advice is to first carefully look where such missing data is observed and to pay attention to replicate measurements where a given element once was measured with a real numeric value and once as missing information (NA).

set.seed(2015); rand1 <- round(runif(300) +rnorm(300,0,2),3)
dat1 <- cbind(ser1=round(100:1 +rand1[1:100]), ser2=round(1.2*(100:1 +rand1[101:200]) -2),
ser3=round((100:1+rand1[201:300])^1.2-3))
dat1 <- cbind(dat1, ser4=round(dat1[,1]^seq(2,5,length.out=100) +rand1[11:110],1))
## Let's introduce some NAs
dat1[dat1 <1] <- NA
## Let's get a quick overview of the data
summary(dat1)
##       ser1             ser2             ser3            ser4
##  Min.   :  2.00   Min.   :  1.00   Min.   :  1.0   Min.   :     37.5
##  1st Qu.: 26.75   1st Qu.: 28.00   1st Qu.: 50.0   1st Qu.:  67210.0
##  Median : 49.50   Median : 59.00   Median :109.0   Median : 332524.0
##  Mean   : 51.14   Mean   : 58.79   Mean   :115.1   Mean   : 542279.4
##  3rd Qu.: 76.25   3rd Qu.: 89.50   3rd Qu.:173.5   3rd Qu.: 925759.5
##  Max.   :100.00   Max.   :121.00   Max.   :263.0   Max.   :2123191.7
##                                    NA's   :1
## some selected lines (indeed, the 4th column appears always much higher)
dat1[c(1:5,50:54,95:100),]
##       ser1 ser2 ser3      ser4
##  [1,]  100  121  251   10000.6
##  [2,]  100  117  244   11500.2
##  [3,]   99  120  263   12948.1
##  [4,]   99  120  242   14885.1
##  [5,]   97  114  236   16382.3
##  [6,]   51   60  111  892534.1
##  [7,]   48   58  109  812490.4
##  [8,]   49   55  108  982907.4
##  [9,]   50   56  107 1188787.7
## [10,]   45   47  102  915343.6
## [11,]    3    6    5     206.4
## [12,]    5    2    7    2570.0
## [13,]    8    1    3   27125.8
## [14,]    6    4    5    6975.9
## [15,]    3    3    1     237.0
## [16,]    2    1   NA      37.5

Our toy data may be normalized by a number of different criteria. In real applications the nature of the data and the type of deformation detected/expected will largely help deciding which normalization might be the ‘best’ choice. Here we’ll try first normalizing by the mean, ie all columns will be forced to end up with the same column-mean. The trimmed mean does not consider values at extremes (as outliers are frequently artefacts and display extreme values). When restricting even stronger which values to consider one will eventually end up with the median (3rd method used below).

no1 <- normalizeThis(dat1, refGrp=1:3, meth="mean")
no2 <- normalizeThis(dat1, refGrp=1:3, meth="trimMean", trim=0.4)
no3 <- normalizeThis(dat1, refGrp=1:3, meth="median")
no4 <- normalizeThis(dat1, refGrp=1:3, meth="slope", quantFa=c(0.2,0.8))

It is suggested to verify normalization results by plots. Note, that Box plots may not be appropriate in some cases (eg multimodal distributions), for more details you may consider using Violin-Plots from packages vioplot or wrGraph, another option might be a (cumulated) frequency plot (eg in package wrGraph).

You can see clearly, that the 4th data-set has a problem of range. So we’ll see if some proportional normalization may help to make it more comparable to the other ones.

### Normalize By Rows

The standard approach for normalizing relies on consisting all columns as collections of data who’s distribution is not supposed to change. In some cases/projects we may want to formulate a much more ‘aggressive’ hypothesis : We consider the content of all columns strictly as the same. For example this may be the case when comparing with tehcnical replicates only. In such cases one may use the function rowNormalize() which tries to find the average or mean optimal within-line normalization factor.

Besides, an additional mode of operation for sparse data has been added : Basically, once a row contains just one NA, this row can’t be used any more to derive a normalization factor for all rows. Thus, with many NA-values the number of ‘complete’ rows will be low or even 0 redering this approach inefficient or impossible. Once the content of NA-values is above a customizable threshold, the data will be broken in smaller subsets with fewer groups of fewer columns, thus increasing the chances of finding ‘complete’ subsets of data which will be normalized first and added to other subsets in later steps.

##                1         2        3          4          5          6         7
##  [1,] 0.04583502 0.2745323 1.534576 -0.2620647  2.6058457 -0.1248548 1.4408823
##  [2,] 1.12222673 0.2745323 0.200466 -0.2620647 -0.3085595 -0.1248548 1.4408823
##  [3,] 0.04583502 1.7320861 1.534576  1.9908371 -0.3085595  0.6670538 1.4408823
##  [4,] 0.04583502 0.2745323 0.200466  1.9908371 -0.3085595  1.4589624 0.1653309
##  [5,] 2.19861844 0.2745323 0.200466 -0.2620647  2.6058457 -0.1248548 1.4408823
##  [6,] 2.19861844 1.7320861 0.200466  1.9908371  1.1486431  0.6670538 0.1653309
##  [7,] 0.04583502 3.1896399 0.200466  1.9908371  2.6058457  0.6670538 0.1653309
##  [8,] 1.12222673 0.2745323 0.200466 -0.2620647 -0.3085595  2.2508710 0.1653309
##  [9,] 0.04583502 0.2745323 2.868686  1.9908371 -0.3085595  0.6670538 0.1653309
## [10,] 0.04583502 0.2745323 0.200466 -0.2620647  1.1486431  0.6670538 0.1653309
##               8        9        10        11
##  [1,] 0.3719772 0.140977 3.7414483 0.2236855
##  [2,] 0.3719772 1.375939 0.4018727 0.2236855
##  [3,] 0.3719772 0.140977 0.4018727 2.9693036
##  [4,] 0.3719772 0.140977 0.4018727 1.5964945
##  [5,] 0.3719772 0.140977 0.4018727 0.2236855
##  [6,] 1.9919393 1.375939 0.4018727 0.2236855
##  [7,] 0.3719772 2.610900 0.4018727 1.5964945
##  [8,] 1.9919393 0.140977 0.4018727 0.2236855
##  [9,] 0.3719772 1.375939 0.4018727 0.2236855
## [10,] 0.3719772 0.140977 0.4018727 0.2236855

Now, let’s make this sparse :

##        1   2   3  4   5   6   7   8   9 10 11
##  [1,] NA  NA 0.5 NA 9.0  NA 0.5  NA  NA  1 NA
##  [2,]  1  NA  NA NA  NA  NA 0.5  NA 0.5 NA NA
##  [3,] NA 0.5 0.5  8  NA 0.5 0.5  NA  NA NA  2
##  [4,] NA  NA  NA  8  NA 1.0  NA  NA  NA NA  1
##  [5,]  2  NA  NA NA 9.0  NA 0.5  NA  NA NA NA
##  [6,]  2 0.5  NA  8 4.5 0.5  NA 0.5 0.5 NA NA
##  [7,] NA 1.0  NA  8 9.0 0.5  NA  NA 1.0 NA  1
##  [8,]  1  NA  NA NA  NA 1.5  NA 0.5  NA NA NA
##  [9,] NA  NA 1.0  8  NA 0.5  NA  NA 0.5 NA NA
## [10,] NA  NA  NA NA 4.5 0.5  NA  NA  NA NA NA
## Warning in rowNormalize(AC, refLines = 1:5, omitNonAlignable = TRUE): ->
## rowNormalize : Incomplete coverage of the initial columns; try using more data /
## more rows
##  -> rowNormalize :  Can't normalize column(s) 1 5 7 9 10 with column(s) 2 3 4 6 11, omit
##               1        5         7         9        10
##  [1,]        NA 4.090909 0.1136364        NA 0.4545455
##  [2,] 0.2272727       NA 0.1136364 0.2272727        NA
##  [3,]        NA       NA 0.1136364        NA        NA
##  [4,]        NA       NA        NA        NA        NA
##  [5,] 0.4545455 4.090909 0.1136364        NA        NA
##  [6,] 0.4545455 2.045455        NA 0.2272727        NA
##  [7,]        NA 4.090909        NA 0.4545455        NA
##  [8,] 0.2272727       NA        NA        NA        NA
##  [9,]        NA       NA        NA 0.2272727        NA
## [10,]        NA 2.045455        NA        NA        NA

### Matrix Coordinates Of Values/Points According To Filtering

Sometimes one needs to obtain the coordinates of values/points of a matrix according to a given filtering condition. The standard approach using which() gives only a linearized index but not row/column, which is sufficient for replacing indexed values. If you need to know the true row/column indexes, you may use coordOfFilt().

## [1]  2  3  6  7  9 14 26
##      row col
## [1,]   2   1
## [2,]   3   1
## [3,]   3   2
## [4,]   1   3
## [5,]   3   3
## [6,]   2   5
## [7,]   2   9

## Statistical Testing

### Normal Random Number Generation with Close Fit to Expected mean and sd

When creating random values to an expected mean and sd, the results ontained using the standard function rnorm() may deviate somehow from the expected mean and sd, in particular with low n. To still produce random values fitting closely to the expected mean and sd you may use the function rnormW(). The case of n=2 is quite simple with one possible results. In other cases (n>2), there will be a random initiation which can be fixed using the argument seed.

## some sample data :
x1 <- (11:16)[-5]
mean(x1); sd(x1)
## [1] 13.2
## [1] 1.923538
## the standard way for gerenating normal random values
ra1 <- rnorm(n=length(x1), mean=mean(x1), sd=sd(x1))
## In particular with low n, the random values deviate somehow from expected mean and sd :
mean(ra1) -mean(x1) 
## [1] -1.103347
sd(ra1) -sd(x1)
## [1] 0.3920622
## random numbers with close fit to expected mean and sd :
ra2 <- rnormW(length(x1), mean(x1), sd(x1))
mean(ra2) -mean(x1) 
## [1] 0
sd(ra2) -sd(x1)   # much closer to expected value
## [1] -4.440892e-16

Thus, the second data-sets fits even with few n very well to the global characteristics defined/expected.

### Moderated Pair-Wise t-Test from limma

If you are not familiar with the way data is handled in the Bioconductor package limma and you would like to use some of the tools for running moderated t-tests therein, this will provide easy access using moderTest2grp() :

set.seed(2017); t8 <- matrix(round(rnorm(1600,10,0.4),2), ncol=8,
dimnames=list(paste("l",1:200), c("AA1","BB1","CC1","DD1","AA2","BB2","CC2","DD2")))
t8[3:6,1:2] <- t8[3:6,1:2]+3     # augment lines 3:6 for AA1&BB1
t8[5:8,5:6] <- t8[5:8,5:6]+3     # augment lines 5:8 for AA2&BB2 (c,d,g,h should be found)
t4 <- log2(t8[,1:4]/t8[,5:8])
fit4 <- moderTest2grp(t4, gl(2,2))
## now we'll use limma's topTable() function to look at the 'best' results
if("list" %in% mode(fit4)) {  # if you have limma installed we can look further
library(limma)
topTable(fit4, coef=1,n=5)                      # effect for 3,4,7,8
fit4in <- moderTest2grp(t4, gl(2,2), testO="<")
if("list" %in% mode(fit4in)) topTable(fit4in, coef=1,n=5) }
##  -> moderTest2grp : testing alternative hypothesis: true difference in means is less than 0 (ie focus on 101 results with A less than B)
##           logFC    AveExpr         t      P.Value    adj.P.Val         B
## l 7  -0.4975806 -0.2436786 -8.712092 3.994695e-17 7.989390e-15 30.668381
## l 4   0.4020373  0.1890232  7.039234 1.000000e+00 1.000000e+00 17.723883
## l 8  -0.3735170 -0.2259811 -6.539873 9.417239e-11 9.417239e-09 14.392733
## l 3   0.3508834  0.1488240  6.143585 1.000000e+00 1.000000e+00 11.923522
## l 27 -0.1348878 -0.1011609 -2.361738 9.333949e-03 6.222633e-01 -3.878176

### Multiple Moderated Pair-Wise t-Tests From limma

If you want to make multiple pair-wise comparisons using moderTestXgrp() :

grp <- factor(rep(LETTERS[c(3,1,4)], c(2,3,3)))
set.seed(2017); t8 <- matrix(round(rnorm(208*8,10,0.4),2), ncol=8,
dimnames=list(paste(letters[],rep(1:8,each=26),sep=""), paste(grp,c(1:2,1:3,1:3),sep="")))
t8[3:6,1:2] <- t8[3:6,1:2] +3                    # augment lines 3:6 (c-f)
t8[5:8,c(1:2,6:8)] <- t8[5:8,c(1:2,6:8)] -1.5    # lower lines
t8[6:7,3:5] <- t8[6:7,3:5] +2.2                  # augment lines
## expect to find C/A in c,d,g, (h)
## expect to find C/D in c,d,e,f
## expect to find A/D in f,g,(h)
test8 <- moderTestXgrp(t8, grp)
head(test8$p.value, n=8)  ## A-C A-D C-D ## a1 8.736828e-02 6.776543e-02 9.397304e-01 ## b1 4.384118e-01 5.400019e-01 8.205610e-01 ## c1 1.094834e-19 6.344497e-01 2.571471e-21 ## d1 2.671725e-13 9.915692e-01 2.858699e-13 ## e1 1.802454e-03 2.413137e-08 9.735465e-16 ## f1 3.188362e-01 2.527208e-32 2.226490e-22 ## g1 1.166242e-29 6.410057e-33 5.484445e-01 ## h1 1.141181e-05 1.943795e-05 5.674938e-01 ### Transform p-values To Local False Discovery Rate (lfdr) To get an introduction into local false discovery rate estimations you may read Strimmer 2008. A convenient way to get lfdr values calculated by the package fdrtool is available via the function pVal2lfdr(). Note, that the toy-example used below is too small for estimating meaningful lfdr values. For this reason the function fdrtool() from package fdrtool will issue warnings. set.seed(2017); t8 <- matrix(round(rnorm(160,10,0.4),2), ncol=8, dimnames=list(letters[1:20], c("AA1","BB1","CC1","DD1","AA2","BB2","CC2","DD2"))) t8[3:6,1:2] <- t8[3:6,1:2] +3 # augment lines 3:6 (c-f) for AA1&BB1 t8[5:8,5:6] <- t8[5:8,5:6] +3 # augment lines 5:8 (e-h) for AA2&BB2 (c,d,g,h should be found) head(pVal2lfdr(apply(t8, 1, function(x) t.test(x[1:4], x[5:8])$p.value)))
## Warning in fdrtool::fdrtool(z, statistic = "pvalue", plot = FALSE, verbose
## = !silent): There may be too few input test statistics for reliable FDR
## calculations!
##         a         b         c         d         e         f
## 1.0000000 0.5753562 0.5753562 1.0000000 1.0000000 1.0000000

### Confindence Intervals (under Normal Distribution)

The confindence interval (CI) is a common way of describing the uncertainity of measured or estimated values. The function confInt() allows calculating the confidence interval of the mean (using the functions qt() and sd()) under a given significance level (alpha). assuming that the Normal distribution is valid.

set.seed(2022); ran <- rnorm(50)
confInt(ran, alpha=0.05)
## [1] 0.248199
## plot points and confindence interval of mean
plot(ran, jitter(rep(1, length(ran))), ylim=c(0.95, 1.05), xlab="random variable 'ran'",main="Points and Confidence Interval of Mean (alpha=0.05)", ylab="", las=1)
points(mean(ran), 0.97, pch=3, col=4)     # mean
lines(mean(ran) +c(-1, 1) *confInt(ran, 0.05), c(0.97, 0.97), lwd=4, col=4)  # CI
legend("topleft","95% conficence interval of mean", text.col=4,col=4,lty=1,lwd=1,seg.len=1.2,cex=0.9,xjust=0,yjust=0.5)

### Extract Groups Of Replicates From Pair-Wise Column-Names

When running multiple pairwise tests (using moderTestXgrp()) the column-names are concatenated group-names. To get the index of which group has been used in which pair-wise set you may use the function matchSampToPairw(), as shown below.

## make example if limma is not installed
if(!requireNamespace("limma", quietly=TRUE)) test8 <- list(FDR=matrix(1,nrow=2,ncol=3,dimnames=list(NULL,c("A-C","A-D","C-D"))))
matchSampToPairw(unique(grp), colnames(test8$FDR))  ## le ri ## A-C 2 1 ## A-D 2 3 ## C-D 1 3 ### Extract Numeric Part Of Column-Names When running multiple pairwise tests (using moderTestXgrp()) the results will be in adjacent columns and the group-names reflected in the column-names. In the case measurements from multiple levels of a given variable are compared it is useful to extract the numeric part, the function numPairDeColNames() provides support to do so. When extracting just the numeric part, unit names will get lost, though. Note, if units used are not constant (eg seconds and milliseconds mixed) the extracted numeric values do not reflect the real quantitative context any more. mat1 <- matrix(1:8, nrow=2, dimnames=list(NULL, paste0(1:4,"-",6:9))) numPairDeColNames(mat1) ## -> numPartDeColNames : PROBLEM ? : 'stripTxt' does REMOVE the separator 'sep' ! Select a different separator or 'stripTxt' strategy to resolve pairwise combinations ! ## index log2rat conc1 conc2 ## [1,] 1 2.585 1 6 ## [2,] 2 1.807 2 7 ## [3,] 3 1.415 3 8 ## [4,] 4 1.170 4 9 ## Working With Clustering Multiple concepts for clustering have been deeveloped, most of them allow extracting a vector with the cluster-numbers. Here some functions helping to work with the output of such clustering results are presented. ### Prepare Data For Clustering The way how to prepare data for clustering may be as important as the choice of the actual clustering-algorithm … Many clustering algorithms are available in R (eg see also CRAN Task View: Cluster Analysis & Finite Mixture Models), many of them require the input data to be standardized. The regular way of standardizing sets all elements to mean=0 and sd=1. To do so, the function scale() may be used. dat <- matrix(2*round(runif(100),2), ncol=4) mean(dat); sd(dat) ## [1] 1.0348 ## [1] 0.5991349 datS <- scale(dat) apply(datS, 2, sd) ## [1] 1 1 1 1 # each column was teated separately mean(datS); sd(datS); range(datS) ## [1] 1.274615e-17 ## [1] 0.9847319 ## [1] -1.898224 1.708967 # the mean is almost 0.0 and the sd almost 1.0 datB <- scale(dat, center=TRUE, scale=FALSE) mean(datB); sd(datB); range(datB) # mean is almost 0 ## [1] 4.435522e-18 ## [1] 0.5815165 ## [1] -1.2096 0.8984 However, if you want the entire data-set and not each column sparately, you may use standardW(). Thus, relative differences visible within a line will be conserved. Furthermore, in case of 3-dim arrays, this function returns also the same dimensions as the input. datS2 <- standardW(dat) apply(datS2, 2, sd) ## [1] 1.1773030 0.9158595 0.9519728 0.8688335 summary(datS2) ## V1 V2 V3 V4 ## Min. :-1.6938 Min. :-1.6270 Min. :-1.6604 Min. :-1.5602 ## 1st Qu.:-1.0929 1st Qu.:-0.5922 1st Qu.:-0.9594 1st Qu.:-1.0595 ## Median : 0.9767 Median : 0.2757 Median :-0.3585 Median :-0.4587 ## Mean : 0.3251 Mean : 0.1115 Mean :-0.1289 Mean :-0.3078 ## 3rd Qu.: 1.3106 3rd Qu.: 0.8098 3rd Qu.: 0.7431 3rd Qu.: 0.3425 ## Max. : 1.5442 Max. : 1.6110 Max. : 1.2438 Max. : 1.1770 mean(datS2); sd(datS2) ## [1] 1.046597e-16 ## [1] 1 datS3 <- standardW(dat, byColumn=TRUE) apply(datS3, 2, sd) ## [1] 0.849399 1.091871 1.050450 1.150969 summary(datS3) ## V1 V2 V3 V4 ## Min. :-1.7149 Min. :-1.97112 Min. :-1.6439 Min. :-1.5952 ## 1st Qu.:-1.0060 1st Qu.:-1.05991 1st Qu.:-0.7672 1st Qu.:-0.6347 ## Median :-0.1696 Median : 0.01531 Median : 0.2672 Median : 0.4987 ## Mean :-0.2762 Mean :-0.12174 Mean : 0.1354 Mean : 0.3542 ## 3rd Qu.: 0.4613 3rd Qu.: 0.82628 3rd Qu.: 1.0474 3rd Qu.: 1.3536 ## Max. : 1.0922 Max. : 1.63725 Max. : 1.8276 Max. : 2.2084 mean(datS3); sd(datS3) ## [1] 0.022922 ## [1] 1.065665 Sometimes it is sufficient to only set the minimum and maximum to a given range. datR2 <- apply(dat, 2, scaleXY, 1, 100) summary(datR2); sd(datR2) ## V1 V2 V3 V4 ## Min. : 1.00 Min. : 1.00 Min. : 1.00 Min. : 1.00 ## 1st Qu.: 19.37 1st Qu.: 32.64 1st Qu.: 24.90 1st Qu.: 19.11 ## Median : 82.65 Median : 59.18 Median : 45.38 Median : 40.84 ## Mean : 62.73 Mean : 54.15 Mean : 53.21 Mean : 46.30 ## 3rd Qu.: 92.86 3rd Qu.: 75.51 3rd Qu.: 82.93 3rd Qu.: 69.82 ## Max. :100.00 Max. :100.00 Max. :100.00 Max. :100.00 ## [1] 32.14382 ### Characterize Clustering Results Here a very basic clustering example… nGr <- 3 irKm <- stats::kmeans(iris[,1:4], nGr, nstart=nGr*4) # no need to standardize table(irKm$cluster, iris$Species) ## ## setosa versicolor virginica ## 1 0 48 14 ## 2 0 2 36 ## 3 50 0 0  #wrGraph::plotPCAw(t(as.matrix(iris[,1:4])), sampleGrp=irKm,colBase=irKm$cluster,useSymb=as.numeric(as.factor(iris$Species))) Using the function reorgByCluNo() we can now ‘apply’ the clustering result to the initial data to obtain other information. ## sort results by cluster number head(reorgByCluNo(iris[,-5], irKm$cluster))
##     Sepal.Length Sepal.Width Petal.Length Petal.Width index  geoMean cluNo
## 118          7.7         3.8          6.7         2.2   118 4.557146     1
## 110          7.2         3.6          6.1         2.5   110 4.458884     1
## 132          7.9         3.8          6.4         2.0   132 4.427465     1
## 136          7.7         3.0          6.1         2.3   136 4.242945     1
## 119          7.7         2.6          6.9         2.3   119 4.221922     1
## 106          7.6         3.0          6.6         2.1   106 4.216232     1
tail(reorgByCluNo(iris[,-5], irKm$cluster)) ## Sepal.Length Sepal.Width Petal.Length Petal.Width index geoMean cluNo ## 23 4.6 3.6 1.0 0.2 23 1.349033 3 ## 33 5.2 4.1 1.5 0.1 33 1.337272 3 ## 38 4.9 3.6 1.4 0.1 38 1.253593 3 ## 10 4.9 3.1 1.5 0.1 10 1.228605 3 ## 13 4.8 3.0 1.4 0.1 13 1.191578 3 ## 14 4.3 3.0 1.1 0.1 14 1.091429 3 Let’s calculate the median and sd values for each cluster: ## median an CV ir2 <- reorgByCluNo(iris[,-5], irKm$cluster, addInfo=FALSE, retList=TRUE)
sapply(ir2, function(x) apply(x, 2, median))
##                1    2   3
## Sepal.Width  2.8 3.00 3.4
## Petal.Length 4.5 5.65 1.5
## Petal.Width  1.4 2.10 0.2
sapply(ir2, colSds)
##                      1         2         3
## Sepal.Width  0.2962841 0.2900924 0.3790644
## Petal.Length 0.5088950 0.4885896 0.1736640
## Petal.Width  0.2974997 0.2798725 0.1053856

Besides, we have already seen the function cutArrayInCluLike() in section Working with Arrays ‘Working with Arrays’.

## Tree-Like Structures

### Filter Lists Of Connected Nodes, Extension Of Networks As ‘Sandwich’

When interogating network-databases (like String for proteins or the coexpressionDB for gene co-expression) typically a (semi-)quantitatve value is supplied with the connection of node ‘A’ to node ‘B’.
In many cases, it may be useful to filter the initial query-output to retain only strong interactions. Furthermore, it may be of interest to expand such networks by nodes allowing to (further) inter-connect initial query-nodes (so called ‘Sandwich’ nodes as they are in the middle of initial nodes), for such nodes a separate (eg even more stringent) threshold can be applied.

Here let’s suppose nodes have 3-digit names (ie numbers). 7 nodes of an initial query gave 1 to 7 conected nodes, the results are presented as list of data.frames where the 1st column is the connected node and the 2nd column the quality score of the connection (edge). Furthemore, let’s assume that here lower scores are better.

lst2 <- list('121'=data.frame(ID=as.character(c(141,221,228,229,449)),11:15),
'131'=data.frame(ID=as.character(c(228,331,332,333,339)),11:15),
'141'=data.frame(ID=as.character(c(121,151,229,339,441,442,449)),c(11:17)),
'151'=data.frame(ID=as.character(c(449,141,551,552)),11:14),
'161'=data.frame(ID=as.character(171),11),
'171'=data.frame(ID=as.character(161),11),
'181'=data.frame(ID=as.character(881:882),11:12) )

Now, we’d like to keep the core network consisting of all (dirctly) interconnected nodes with scores below 20 :

(nw1 <- filterNetw(lst2, limInt=20, sandwLim=NULL, remOrphans=FALSE))
##  -> filterNetw : Invalid entry for 'filtCol': should be integer (of length=1) to designate which column to use or column-name; setting to 2
##  -> filterNetw : 2 element(s) had no data remaining after filtering ...
## -> filterNetw -> .filterNetw : Removing 3 (reverse) redundant mappings
##   Node1 Node2 edgeScore toSandw
## 1   121   141        11   FALSE
## 2   141   151        12   FALSE
## 3   161   171        11   FALSE

In the resulting output the 1st column now represents the query-nodes, the 2nd column all connected nodes based on filtering scores for edges, and the 3rd colum the score for the edges.

Let’s also remove all nodes not connected to a backbone at least 3 nodes long, ie remove orphan pairs of nodes :

(nw2 <- filterNetw(lst2, limInt=20, sandwLim=NULL, remOrphans=TRUE))
##  -> filterNetw : Invalid entry for 'filtCol': should be integer (of length=1) to designate which column to use or column-name; setting to 2
##  -> filterNetw : 2 element(s) had no data remaining after filtering ...
## -> filterNetw -> .filterNetw : Removing 3 (reverse) redundant mappings
##   Node1 Node2 edgeScore toSandw
## 1   121   141        11   FALSE
## 2   141   151        12   FALSE

If you want to expand this network by nodes allowing to further interconnect the nodes from above, we can add all ‘sandwich’ nodes (let’s use a threshold of inferior/equal to 14 which will use only the better ‘sandwich’-edges) :

(nw3 <- filterNetw(lst2, limInt=20, sandwLim=14, remOrphans=TRUE))
##  -> filterNetw : Invalid entry for 'filtCol': should be integer (of length=1) to designate which column to use or column-name; setting to 2
##  -> filterNetw : 1 element(s) had no data remaining after filtering ...
## -> filterNetw -> .filterNetw : Removing 3 (reverse) redundant mappings
##   Node1 Node2 edgeScore toSandw
## 1   121   141        11   FALSE
## 2   121   228        13    TRUE
## 3   121   229        14    TRUE
## 4   131   228        11    TRUE
## 5   141   151        12   FALSE
## 6   141   229        13    TRUE

### Convert Collection Of Pairs Of Nodes To Propensity Matrix

Many times networks get created from pairs of nodes. One way to represent the full network is via propensisty matrixes. Several advanced tools and packages rather accept such propensisty matrixes as input. Here, it is assumed that each line of the input represents a separate pair of nodes connected by an edge.

pairs3L <- matrix(LETTERS[c(1,3,3, 2,2,1)], ncol=2)      # loop of 3
(netw13pr <- pairsAsPropensMatr(pairs3L))                # as prop matr
##   1 2 3
## 1 0 1 1
## 2 1 0 1
## 3 1 1 0

### Characterize Individual Contribution Of Single Edges In Tree-Structures

path1 <- matrix(c(17,19,18,17, 4,4,2,3), ncol=2,
dimnames=list(c("A/B/C/D","A/B/G/D","A/H","A/H/I"), c("sumLen","n")))
contribToContigPerFrag(path1)
##   sumLe n.frag len.rat
## A    19      4   1.000
## B    19      4   1.000
## C    17      4   0.895
## D    19      4   1.000
## G    19      4   1.000
## H    18      2   0.947
## I    17      3   0.895

### Count Same Start- And End- Sites Of Edges (Or Fragments)

If you have a set of fragments from a common ancestor and the fragment’s start- and end-sites are marked by index-positions (integers), you can make a simple graphical display :

frag1 <- cbind(beg=c(2,3,7,13,13,15,7,9,7, 3,3,5), end=c(6,12,8,18,20,20,19,12,12, 4,5,7))
rownames(frag1) <- letters[1:nrow(frag1)]
simpleFragFig(frag1)

Now we can make a matrix telling if some fragments do start or end at exactely the same position.

countSameStartEnd(frag1)
##   beg end beg.n beg.rat end.n end.rat
## a   2   6    NA      NA    NA      NA
## b   3  12     3  0.2500     3  0.2500
## c   7   8     3  0.2500    NA      NA
## d  13  18     2  0.1667    NA      NA
## e  13  20     2  0.1667     2  0.1667
## f  15  20    NA      NA     2  0.1667
## g   7  19     3  0.2500    NA      NA
## h   9  12    NA      NA     3  0.2500
## i   7  12     3  0.2500     3  0.2500
## j   3   4     3  0.2500    NA      NA
## k   3   5     3  0.2500    NA      NA
## l   5   7    NA      NA    NA      NA

## Support for Graphical Output

### Convenient Paste-Collapse

The function pasteC() allows adding quotes and separating the last element by specific text (eg ‘and’).

pasteC(1:4)
## [1] "1, 2, 3 and 4"
pasteC(letters[1:4],quoteC="'")
## [1] "'a', 'b', 'c' and 'd'"

### Transform Numeric Values to Color-Gradient

By default most color-gradients end with a color very close to the beginning.

set.seed(2015); dat1 <- round(runif(15),2)
plot(1:15, dat1, pch=16, cex=2, las=1, col=colorAccording2(dat1),
main="Color gradient according to value in y")

# Here we modify the span of the color gradient
plot(1:15, dat1, pch=16, cex=2, las=1,
col=colorAccording2(dat1,nStartO=0,nEndO=4,revCol=TRUE), main="blue to red")

# It is also possible to work with scales of transparency
plot(1:9, pch=3, las=1)
points(1:9, 1:9, col=transpGraySca(st=0, en=0.8, nSt=9,trans=0.3), cex=42, pch=16)

### Assign New Transparency To Given Colors

For this purpose you may use convColorToTransp.

col0 <- c("#998FCC","#5AC3BA","#CBD34E","#FF7D73")
col1 <- convColorToTransp(col0,alph=0.7)
layout(1:2)
pie(rep(1,length(col0)), col=col0, main="no transparency")
pie(rep(1,length(col1)), col=col1, main="new transparency")

## Other Convenience Functions

### Writing Compact Dates (more options …)

Many times it may be useful to add the date to filenames when saving data or plots as files. The built-in functions date(), Sys.Date() and Sys.Time() are a good way to start.

Generally I like to use abbreviated month-names since the order of writing the month is different in Europe compared to the USA, so this may help avoiding mis-interpreting dates insetad of writing the number of the Month. For example, 2021-03-05 means in Europe March 5th while in other places it means May 3rd.

The R-functions mentioned above use local language settings, so I wrote the function sysDate to produce compact versions of current the date, independent to local language settings (or not -if you prefer), ie locale-specific, (yes, in some languages - like French - the first 3 letters of the month may give ambiguous results !) and to avoid white space ’ ’ (which I prefer to avoid in file-names). Please look at the function’s help-page for all available options.

## To get started
Sys.Date()
## [1] "2022-10-03"
## Compact English names (in European order), no matter what your local settings are :
sysDate() 
## [1] "03oct22"

The table below shows a number of options to write the date in English or using local month-names :

tabD <- cbind(paste0("univ",1:6), c(sysDate(style="univ1"), sysDate(style="univ2"),
sysDate(style="univ3"), sysDate(style="univ4"), as.character(sysDate(style="univ5")),
sysDate(style="univ6")), paste0("   local",1:6),
c(sysDate(style="local1"), sysDate(style="local2"), sysDate(style="local3"),
sysDate(style="local4"), sysDate(style="local5"), sysDate(style="local6")))
knitr::kable(tabD, caption="Various ways of writing current date")
 univ1 03oct22 local1 03oct22 univ2 03Oct22 local2 03Oct22 univ3 03October2022 local3 03Oct.2022 univ4 03october2022 local4 03oct.2022 univ5 2022-10-03 local5 3-oct.-2022 univ6 2022-276 local6 2022oct.03

## Session-Info

## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C                   LC_CTYPE=French_France.utf8
## [3] LC_MONETARY=French_France.utf8 LC_NUMERIC=C
## [5] LC_TIME=French_France.utf8
##
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base
##
## other attached packages:
## [1] limma_3.52.4  knitr_1.40    wrMisc_1.10.0
##
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.9        plyr_1.8.7        bslib_0.4.0       compiler_4.2.1
##  [5] pillar_1.8.1      jquerylib_0.1.4   highr_0.9         tools_4.2.1
##  [9] digest_0.6.29     jsonlite_1.8.0    evaluate_0.16     lifecycle_1.0.2
## [13] tibble_3.1.8      checkmate_2.1.0   gtable_0.3.1      pkgconfig_2.0.3
## [17] rlang_1.0.6       wrGraph_1.3.1     DBI_1.1.3         cli_3.4.1
## [21] yaml_2.3.5        xfun_0.33         fastmap_1.1.0     dplyr_1.0.10
## [25] stringr_1.4.1     generics_0.1.3    vctrs_0.4.2       sass_0.4.2
## [29] tidyselect_1.1.2  grid_4.2.1        qvalue_2.28.0     glue_1.6.2
## [33] data.table_1.14.2 R6_2.5.1          fansi_1.0.3       fdrtool_1.2.17
## [37] rmarkdown_2.16    reshape2_1.4.4    purrr_0.3.4       ggplot2_3.3.6
## [41] magrittr_2.0.3    splines_4.2.1     backports_1.4.1   BBmisc_1.13
## [45] scales_1.2.1      htmltools_0.5.3   assertthat_0.2.1  colorspace_2.0-3
## [49] utf8_1.2.2        stringi_1.7.8     munsell_0.5.0     cachem_1.0.6