# Pair-wise testing robust to NA-imputation This function replaces `NA` values based on group neighbours (based on grouping of columns in argument `gr`), following overall assumption of close to Gaussian distribution. Furthermore, it is assumed that `NA`-values originate from experimental settings where measurements at or below detection limit are recoreded as `NA`. In such cases (eg in proteomics) it is current practice to replace `NA`-values by very low (random) values in order to be able to perform t-tests. However, random normal values used for replacing may in rare cases deviate from the average (the 'assumed' value) and in particular, if multiple `NA` replacements are above the average, may look like induced biological data and be misinterpreted as so. The statistical testing uses `eBayes` from Bioconductor package [limma](https://bioconductor.org/packages/release/bioc/html/limma.html) for robust testing in the context of small numbers of replicates. By repeating multiple times the process of replacing `NA`-values and subsequent testing the results can be sumarized afterwards by median over all repeated runs to remmove the stochastic effect of individual NA-imputation. Thus, one may gain stability towards random-character of `NA` imputations by repeating imputation & test 'nLoop' times and summarize p-values by median (results stabilized at 50-100 rounds). It is necessary to define all groups of replicates in `gr` to obtain all possible pair-wise testing (multiple columns in $BH, $lfdr etc). The modified testing-procedure of Bioconductor package [ROTS](https://bioconductor.org/packages/release/bioc/html/ROTS.html) may optionaly be included, if desired. This function returns a [limma](https://bioconductor.org/packages/release/bioc/html/limma.html)-like S3 list-object further enriched by additional fields/elements. ```r testRobustToNAimputation( dat, gr = NULL, annot = NULL, retnNA = TRUE, avSd = c(0.15, 0.5), avSdH = NULL, plotHist = FALSE, xLab = NULL, tit = NULL, imputMethod = "mode2", seedNo = NULL, multCorMeth = NULL, nLoop = 100, lfdrInclude = NULL, ROTSn = NULL, silent = FALSE, debug = FALSE, callFrom = NULL ) ``` ## Arguments - `dat`: (matrix or data.frame) main data (may contain `NA`); if `dat` is list containing $quant and $annot as matrix, the element $quant will be used - `gr`: (character or factor) replicate association; if `dat` contains a list-element `$sampleSetup$groups` or `$sampleSetup$lev` this may be used in case `gr=NULL` - `annot`: (matrix or data.frame) annotation (lines must match lines of data !), if `annot` is `NULL` and argument `dat` is a list containing both $quant and $annot, the element $annot will be used - `retnNA`: (logical) retain and report number of `NA` - `avSd`: (numerical,length=2) population characteristics (mean and sd) for >1 `NA`-neighbours (per line) - `avSdH`: depreciated, please use `avSd` inestad; (numerical,length=2) population characteristics 'high' (mean and sd) for >1 `NA`-neighbours (per line) - `plotHist`: (logical) additional histogram of original, imputed and resultant distribution (made using `matrixNAneighbourImpute` ) - `xLab`: (character) custom x-axis label - `tit`: (character) custom title - `imputMethod`: (character) choose the imputation method (may be 'mode2'(default), 'mode1', 'datQuant', 'modeAdopt', 'informed' or 'none', for details see `matrixNAneighbourImpute` ) - `seedNo`: (integer) seed-value for normal random values - `multCorMeth`: (character) define which method(s) for correction of multipl testing should be run (for choice : 'BH','lfdr','BY','tValTab', choosing several is possible) - `nLoop`: (integer) number of runs of independent `NA`-imputation - `lfdrInclude`: (logical) depreciated, please used `multCorMeth` instead (include lfdr estimations, may cause warning message(s) concerning convergence if few too lines/proteins in dataset tested). - `ROTSn`: (integer) depreciated, please used `multCorMeth` instead (number of repeats by `ROTS`, if `NULL` `ROTS` will not be called) - `silent`: (logical) suppress messages - `debug`: (logical) additional messages for debugging - `callFrom`: (character) This function allows easier tracking of messages produced ## Returns This function returns a limma-type S3 object of class 'MArrayLM' (which can be accessed lika a list); multiple results of testing or multiple testing correction types may get included ('p.value','FDR','BY','lfdr' or 'ROTS.BH') ## Details The argument `multCorMeth` allows to choose which multiple correction algorimths will be used and included to the final results. Possible options are 'lfdr','BH','BY','tValTab', ROTSn='100' (name to element necessary) or 'noLimma' (to add initial p.values and BH to limma-results). By default 'lfdr' (local false discovery rate from package 'fdrtools') and 'BH' (Benjamini-Hochberg FDR) are chosen. The option 'BY' referrs to Benjamini-Yakuteli FDR, 'tValTab' allows exporting all individual t-values from the repeated NA-substitution and subsequent testing. This function is compatible with automatic extraction of experimental setup based on sdrf or other quantitation-specific sample annotation. In this case, the results of automated importing and mining of sample annotation should be stored as `$sampleSetup$groups` or `$sampleSetup$lev` For details 'on choice of NA-impuation procedures with arguments 'imputMethod' and 'avSd' please see `matrixNAneighbourImpute`. ## Examples ```r set.seed(2015); rand1 <- round(runif(600) +rnorm(600,1,2),3) dat1 <- matrix(rand1,ncol=6) + matrix(rep((1:100)/20,6),ncol=6) dat1[13:16,1:3] <- dat1[13:16,1:3] +2 # augment lines 13:16 dat1[19:20,1:3] <- dat1[19:20,1:3] +3 # augment lines 19:20 dat1[15:18,4:6] <- dat1[15:18,4:6] +1.4 # augment lines 15:18 dat1[dat1 <1] <- NA # mimick some NAs for low abundance ## normalize data boxplot(dat1, main="data before normalization") dat1 <- wrMisc::normalizeThis(as.matrix(dat1), meth="median") ## designate replicate relationships in samples ... grp1 <- gl(2, 3, labels=LETTERS[1:2]) ## moderated t-test with repeated inputations (may take >10 sec, >60 sec if ROTSn >0 !) PLtestR1 <- testRobustToNAimputation(dat=dat1, gr=grp1, retnNA=TRUE, nLoop=70) names(PLtestR1) ``` ## See Also NA-imputation via `matrixNAneighbourImpute`, modereated t-test without NA-imputation `moderTest2grp`, calculating lfdr `pVal2lfdr`, `eBayes` in Bioconductor package [limma](https://bioconductor.org/packages/release/bioc/html/limma.html), `t.test`,`ROTS` of Bioconductor package [ROTS](https://bioconductor.org/packages/release/bioc/html/ROTS.html)

testRobustToNAimputation function