removeOutliers: This function Remove Outliers

In COMBIA: Synergy/Antagonism Analyses of Drug Combinations

Description

This function Remove Outliers

Usage

removeOutliers(arrangeReplicates, minThersholdForCVCal, minThersholdForCV)

Arguments

arrangeReplicates	A data matrix
minThersholdForCVCal	Threshold for value removal in CV
minThersholdForCV	Values to be excluded

Value

Replicate values

Author(s)

Muhammad kashif

Examples

dataFile <- system.file("extdata", "rawDataPreProcessed.csv", package="COMBIA")
dataSample <- read.csv(dataFile, header=FALSE )
minThersholdForCV <- 0.3
minThersholdForCVCal <- 0.1
removeOutliers( as.matrix(dataSample ), minThersholdForCV,
       minThersholdForCVCal) 

Example output

Loading required package: hash
hash-3.0.1 provided by Decision Patterns

Loading required package: lattice
Loading required package: latticeExtra
Loading required package: RColorBrewer
Loading required package: oro.nifti
oro.nifti 0.9.1
            V1        V2        V3        V4        V5        V6        V7
[1,] 0.4959273 0.4544325 0.4527889 0.5141335 0.7657826 0.8754745 0.9742680
[2,] 0.5282520 0.4508880 0.4907902 0.5426933 0.7353865 0.9182928 0.9180664
[3,] 0.4939636 0.4681056 0.5032695 0.5585093 0.7629166 0.8590776 0.9670757
[4,] 0.5893432 0.5392455 0.0000000 0.6414779 0.8667800 0.9914462 1.0411311
            V8        V9       V10       V11       V12       V13       V14
[1,] 0.9653167 0.3883595 0.4311439 0.5377761 0.5499641 0.7446938 0.8205780
[2,] 0.8856867 0.4417552 0.0000000 0.5198489 0.6046601 0.7495008 0.8080710
[3,] 1.0026615 0.0000000 0.4016490 0.5368700 0.5787590 0.7088183 0.8592761
[4,] 0.9958504 0.4488219 0.0000000 0.5606334 0.6452214 0.7078161 0.8624584
           V15       V16       V17       V18       V19       V20       V21
[1,] 0.9662776 0.9516621 0.0000000 0.4589335 0.5391416 0.5714069 0.6952844
[2,] 0.8874478 0.8987191 0.4345346 0.5168550 0.5483290 0.5782179 0.6729921
[3,] 0.9738752 0.9966561 0.0000000 0.0000000 0.5139402 0.5712646 0.7147492
[4,] 0.9743249 0.8783960 0.4989471 0.0000000 0.5916279 0.6235858 0.7095227
           V22       V23       V24        V25       V26       V27       V28
[1,] 0.8367865 0.9272861 0.9249851 0.14439735 0.2005835 0.3076203 0.5062694
[2,] 0.8237954 0.8538857 0.8311670 0.08167886 0.2314255 0.3779770 0.0000000
[3,] 0.8314577 0.9330037 0.9559334 0.06050699 0.1120493 0.0000000 0.4452006
[4,] 0.8354551 0.8611646 0.8680737 0.05142586 0.0000000 0.0000000 0.0000000
           V29       V30       V31       V32        V33        V34        V35
[1,] 0.4562531 0.0000000 0.6713130 0.7385240 0.05020592 0.05465631 0.06740060
[2,] 0.4803995 0.5218113 0.6356309 0.6285863 0.03475723 0.05490963 0.06658343
[3,] 0.4499404 0.6100268 0.6939040 0.0000000 0.02834578 0.04132442 0.06430379
[4,] 0.4855143 0.5217113 0.6216590 0.6994481 0.03135925 0.05079276 0.13865644
            V36       V37       V38       V39       V40        V41        V42
[1,] 0.12429475 0.1606059 0.2181069 0.2718402 0.3368766 0.02754941 0.04739915
[2,] 0.08167886 0.1406768 0.2105184 0.2518295 0.3291433 0.03548684 0.04884630
[3,] 0.08065737 0.1508611 0.1835683 0.2876951 0.3136276 0.03569124 0.04281336
[4,] 0.13160973 0.2082152 0.2306766 0.0000000 0.3092915 0.05068265 0.04264499
            V43        V44       V45       V46       V47       V48        V49
[1,] 0.06808333 0.08942496 0.1572175 0.1623506 0.2106727 0.2543674 0.01933137
[2,] 0.07347701 0.11561842 0.1250531 0.1410291 0.2056878 0.2528611 0.03053051
[3,] 0.08353600 0.08701020 0.1579832 0.2060017 0.0000000 0.2390066 0.02948731
[4,] 0.11102012 0.08302596 0.1467216 0.1699538 0.2212626 0.2220058 0.04085579
            V50        V51        V52       V53       V54       V55       V56
[1,] 0.03738578 0.06740060 0.09645455 0.1303635 0.1664470 0.1984847 0.2228354
[2,] 0.04690905 0.06693566 0.07219390 0.1162222 0.1463879 0.1632948 0.1580617
[3,] 0.05544457 0.08321339 0.11244634 0.1846601 0.1907400 0.2458806 0.2747413
[4,] 0.05445374 0.08222770 0.12626965 0.1550896 0.2057103 0.2272083 0.2090685
            V57        V58        V59        V60        V61       V62       V63
[1,] 0.01025359 0.01930608 0.04671642 0.04841060 0.10983099 0.1492017 0.1828325
[2,] 0.03060599 0.03719766 0.05735006 0.04758835 0.10417106 0.1408530 0.1359973
[3,] 0.01926322 0.05350895 0.05651165 0.08440455 0.09614239 0.1644601 0.1522012
[4,] 0.02222054 0.04581051 0.04608577 0.06967574 0.11129538 0.1729541 0.1654670
            V64          V65         V66         V67         V68         V69
[1,] 0.08573316 -0.000568941 0.001226385 0.006915800 0.001555107 0.007674388
[2,] 0.17184891  0.003031666 0.001496964 0.002729757 0.015233806 0.041273425
[3,] 0.19587686  0.010081396 0.008840608 0.015441596 0.042292230 0.029065439
[4,] 0.00000000  0.003282502 0.011320157 0.007466487 0.002208979 0.045507721
             V70        V71        V72         V73         V74         V75
[1,] 0.005423909 0.03877652 0.00643536 0.000265506 0.000670087 0.000316079
[2,] 0.012994651 0.04655683 0.02595156 0.001673077 0.000943464 0.006176547
[3,] 0.012165918 0.04110108 0.02296077 0.002115542 0.001718490 0.001718490
[4,] 0.019302761 0.02464285 0.09648628 0.001135457 0.000584932 0.000172039
              V76         V77          V78         V79          V80
[1,] -0.000214933 0.000391937 -0.000240220 0.001605679  0.001175812
[2,]  0.000515761 0.000515761  0.000138375 0.002075622  0.001018942
[3,] -0.000018600 0.001395886  0.000179914 0.000725860  0.000130283
[4,]  0.000172039 0.001465771  0.000860194 0.001273088 -0.000295907

COMBIA documentation built on May 2, 2019, 7:23 a.m.