This is an quick start manual of BALLI
data <- data.frame(read.table("counts.txt"))
or make example count data
GenerateData <- function(nRow) {
expr_mean <- runif(1,10,100)
expr_size <- runif(1,1,10)
expr <- rnbinom(20,mu=expr_mean,size=expr_size)
return(expr)
}
data <- data.frame(t(sapply(1:10000,GenerateData)))
colnames(data) <- c(paste0("A",1:10),paste0("B",1:10))
rownames(data) <- paste0("gene",1:10000)
head(data)
## A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10
## gene1 17 23 43 35 22 50 20 24 21 39 13 34 33 27 13 14 29 28 42 36
## gene2 180 182 84 32 60 67 98 73 85 51 49 64 82 132 55 57 68 79 34 43
## gene3 24 31 24 9 45 5 40 36 5 28 52 0 26 2 14 3 9 19 13 20
## gene4 51 14 75 93 67 83 85 85 81 91 119 102 72 134 68 52 46 59 27 87
## gene5 50 76 22 101 156 165 46 145 146 63 80 237 37 27 30 44 95 65 14 39
## gene6 56 68 50 106 43 68 62 63 66 54 96 41 89 67 22 28 99 111 116 35
## [1] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "B" "B" "B" "B" "B" "B" "B" "B" "B"
## [20] "B"
## (Intercept) GroupB
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 1 0
## 6 1 0
## An object of class "DGEList"
## $counts
## A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 B1 B2 B3 B4 B5 B6 B7 B8 B9 B10
## gene1 17 23 43 35 22 50 20 24 21 39 13 34 33 27 13 14 29 28 42 36
## gene2 180 182 84 32 60 67 98 73 85 51 49 64 82 132 55 57 68 79 34 43
## gene3 24 31 24 9 45 5 40 36 5 28 52 0 26 2 14 3 9 19 13 20
## gene4 51 14 75 93 67 83 85 85 81 91 119 102 72 134 68 52 46 59 27 87
## gene5 50 76 22 101 156 165 46 145 146 63 80 237 37 27 30 44 95 65 14 39
## 9995 more rows ...
##
## $samples
## group lib.size norm.factors
## A1 A 553271 0.9982835
## A2 A 545459 1.0000239
## A3 A 549812 1.0071814
## A4 A 558730 0.9955053
## A5 A 548118 1.0035048
## 15 more rows ...
## An object of class "TecVarList"
## $targets
## group lib.size norm.factors
## A1 A 552321.3 0.9982835
## A2 A 545472.0 1.0000239
## A3 A 553760.4 1.0071814
## A4 A 556218.7 0.9955053
## A5 A 550039.0 1.0035048
## 15 more rows ...
##
## $design
## (Intercept) GroupB
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 1 0
## 15 more rows ...
##
## $logcpm
## A1 A2 A3 A4 A5 A6 A7 A8
## gene1 5.104717 5.517122 6.344837 6.056465 5.447149 6.559439 5.300322 5.547756
## gene2 8.364244 8.397819 7.279092 5.934541 6.816503 6.967513 7.483244 7.075441
## gene3 5.557127 5.917938 5.553663 4.308206 6.416872 3.666615 6.232291 6.094908
## gene4 6.584466 4.872620 7.119634 7.416412 6.970839 7.268374 7.282395 7.289515
## gene5 6.556988 7.159447 5.438233 7.533034 8.166134 8.242675 6.424810 8.046114
## A9 A10 B1 B2 B3 B4 B5 B6
## gene1 5.387427 6.229271 4.779934 6.035532 5.996556 5.734514 4.765932 4.863216
## gene2 7.307078 6.599775 6.546877 6.910150 7.259846 7.943575 6.691821 6.746192
## gene3 3.670382 5.778390 6.629371 1.860013 5.674518 2.868908 4.859032 3.184138
## gene4 7.239169 7.411212 7.793654 7.566261 7.076958 7.964953 6.988206 6.618425
## gene5 8.073628 6.894307 7.232225 8.766753 6.152720 5.734514 5.858962 6.387076
## B7 B8 B9 B10
## gene1 5.816117 5.761478 6.312035 6.099061
## gene2 6.991277 7.194193 6.022607 6.342910
## gene3 4.321113 5.247072 4.760178 5.310925
## gene4 6.446930 6.785128 5.710768 7.326584
## gene5 7.461923 6.920463 4.853222 6.208650
## 9995 more rows ...
##
## $tecVar
## A1 A2 A3 A4 A5 A6
## gene1 0.03137790 0.03176540 0.03129765 0.03116134 0.03150596 0.03143247
## gene2 0.01132799 0.01146054 0.01130053 0.01125394 0.01137181 0.01134666
## gene3 0.04362898 0.04415640 0.04351979 0.04333455 0.04380330 0.04370327
## gene4 0.01363335 0.01379532 0.01360001 0.01354349 0.01368689 0.01365617
## gene5 0.01111876 0.01124893 0.01109181 0.01104608 0.01116177 0.01113709
## A7 A8 A9 A10 B1 B2
## gene1 0.03100630 0.03116108 0.03154568 0.03172004 0.03503363 0.03481867
## gene2 0.01120094 0.01125385 0.01138540 0.01144504 0.01452320 0.01443646
## gene3 0.04312384 0.04333420 0.04385736 0.04409468 0.09091585 0.09038860
## gene4 0.01347919 0.01354338 0.01370349 0.01377637 0.01303535 0.01295873
## gene5 0.01099404 0.01104599 0.01117512 0.01123370 0.01802903 0.01791872
## B3 B4 B5 B6 B7 B8
## gene1 0.03486052 0.03509793 0.03465286 0.03476539 0.03472721 0.03454280
## gene2 0.01445335 0.01454914 0.01436968 0.01441498 0.01439961 0.01432537
## gene3 0.09049127 0.09107344 0.08998262 0.09025803 0.09016458 0.08971319
## gene4 0.01297365 0.01305827 0.01289973 0.01293975 0.01292617 0.01286057
## gene5 0.01794020 0.01806203 0.01783416 0.01789151 0.01787205 0.01777808
## B9 B10
## gene1 0.03449717 0.03445951
## gene2 0.01430700 0.01429184
## gene3 0.08960146 0.08950925
## gene4 0.01284434 0.01283093
## gene5 0.01775482 0.01773563
## 9995 more rows ...
## An object of class "Balli"
## $Result
## log2FC_GroupB lLLI lBALLI pLLI pBALLI BCF
## gene1 -0.1328193 0.3263385 0.29008132 0.5678231 0.5901684 0.1249898
## gene2 -0.3576113 1.6048412 1.42653031 0.2052185 0.2323315 0.1249962
## gene3 -0.8480371 2.3977512 2.13144570 0.1215098 0.1443046 0.1249412
## gene4 0.0821774 0.0712518 0.06333494 0.7895228 0.8013005 0.1249999
## gene5 -0.6959010 2.4667294 2.19265400 0.1162797 0.1386702 0.1249971
## 9995 more rows ...
##
## $topGenes
## log2FC_GroupB pLLI pBALLI adjpLLI adjpBALLI
## gene3994 0.9241414 1.892826e-05 5.516397e-05 0.1892826 0.5167442
## gene9264 -1.2869402 6.675543e-05 1.702285e-04 0.2132637 0.5167442
## gene2832 1.1190097 8.196976e-05 2.045314e-04 0.2132637 0.5167442
## gene9289 0.7366361 9.678409e-05 2.372973e-04 0.2132637 0.5167442
## gene8915 0.7753783 1.066318e-04 2.583721e-04 0.2132637 0.5167442
## 9995 more rows ...