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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
## gene1 7 39 16 80 48 40 128 48 14 59 160 46 23 63 24 108 40 19
## gene2 33 39 112 24 74 61 167 124 89 95 95 151 206 54 224 66 45 110
## gene3 82 68 39 159 43 103 67 102 182 47 97 97 93 60 74 82 70 109
## gene4 13 18 14 15 6 20 17 11 11 6 13 15 2 12 12 17 13 14
## gene5 10 14 40 26 19 22 19 22 27 20 13 9 17 25 18 12 15 54
## gene6 50 71 20 13 22 10 26 23 10 40 13 22 16 43 42 30 35 43
## B9 B10
## gene1 22 68
## gene2 75 62
## gene3 56 34
## gene4 5 20
## gene5 16 23
## gene6 26 33## [1] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "B" "B" "B" "B" "B" "B" "B"
## [18] "B" "B" "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
## gene1 7 39 16 80 48 40 128 48 14 59 160 46 23 63 24 108 40 19
## gene2 33 39 112 24 74 61 167 124 89 95 95 151 206 54 224 66 45 110
## gene3 82 68 39 159 43 103 67 102 182 47 97 97 93 60 74 82 70 109
## gene4 13 18 14 15 6 20 17 11 11 6 13 15 2 12 12 17 13 14
## gene5 10 14 40 26 19 22 19 22 27 20 13 9 17 25 18 12 15 54
## B9 B10
## gene1 22 68
## gene2 75 62
## gene3 56 34
## gene4 5 20
## gene5 16 23
## 9995 more rows ...
##
## $samples
## group lib.size norm.factors
## A1 A 551929 0.9939824
## A2 A 554250 1.0019442
## A3 A 551469 0.9964722
## A4 A 551560 1.0071093
## A5 A 546462 0.9998596
## 15 more rows ...## An object of class "TecVarList"
## $targets
## group lib.size norm.factors
## A1 A 548607.7 0.9939824
## A2 A 555327.6 1.0019442
## A3 A 549523.5 0.9964722
## A4 A 555481.2 1.0071093
## A5 A 546385.3 0.9998596
## 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
## gene1 4.034259 6.206591 5.033024 7.205971 6.515297 6.237959 7.888394
## gene2 5.994961 6.206591 7.696526 5.549376 7.119559 6.822715 8.266935
## gene3 7.258266 6.978131 6.221008 8.179219 6.363232 7.559515 6.974440
## gene4 4.771948 5.171450 4.863019 4.936799 3.868508 5.305636 5.113222
## gene5 4.449749 4.849758 6.255780 5.656237 5.262988 5.431069 5.257693
## A8 A9 A10 B1 B2 B3 B4
## gene1 6.510839 4.849230 6.797026 8.221498 6.447280 5.477148 6.882641
## gene2 7.844489 7.355990 7.466305 7.481365 8.119834 8.531824 6.667616
## gene3 7.567617 8.371645 6.480929 7.510818 7.491771 7.401550 6.814466
## gene4 4.566359 4.549959 3.864428 4.785661 4.949437 2.844617 4.667364
## gene5 5.451541 5.706648 5.325222 4.785661 4.321116 5.081903 5.615058
## B5 B6 B7 B8 B9 B10
## gene1 5.559786 7.627629 6.240335 5.251725 5.455301 6.978569
## gene2 8.679625 6.933867 6.402553 7.666884 7.137895 6.849310
## gene3 7.107348 7.238655 7.017734 7.653945 6.728975 6.019453
## gene4 4.666604 5.095240 4.755808 4.859361 3.674976 5.309277
## gene5 5.181240 4.655118 4.936215 6.666856 5.039891 5.493604
## 9995 more rows ...
##
## $tecVar
## A1 A2 A3 A4 A5 A6
## gene1 0.02433482 0.02404494 0.02429490 0.02403839 0.02443225 0.02398665
## gene2 0.01290915 0.01276419 0.01288908 0.01276092 0.01295810 0.01273506
## gene3 0.01157954 0.01144914 0.01156159 0.01144619 0.01162334 0.01142290
## gene4 0.06724111 0.06647979 0.06713630 0.06646259 0.06749685 0.06632664
## gene5 0.04159129 0.04110131 0.04152382 0.04109025 0.04175595 0.04100278
## A7 A8 A9 A10 B1 B2
## gene1 0.02433497 0.02435499 0.02403503 0.02434190 0.01994302 0.019683524
## gene2 0.01290922 0.01291928 0.01275924 0.01291270 0.00991243 0.009788362
## gene3 0.01157961 0.01158861 0.01144467 0.01158272 0.01245367 0.012299260
## gene4 0.06724149 0.06729405 0.06645375 0.06725969 0.07659121 0.075636970
## gene5 0.04159154 0.04162538 0.04108456 0.04160325 0.04821743 0.047601141
## B3 B4 B5 B6 B7
## gene1 0.019272708 0.019654898 0.019643162 0.019466946 0.019486113
## gene2 0.009591265 0.009774668 0.009769053 0.009684516 0.009693714
## gene3 0.012053776 0.012282216 0.012275228 0.012169979 0.012181437
## gene4 0.074120695 0.075531652 0.075488473 0.074838233 0.074909017
## gene5 0.046619411 0.047532925 0.047504959 0.047083888 0.047129718
## B8 B9 B10
## gene1 0.019644458 0.019802616 0.019505915
## gene2 0.009769673 0.009845315 0.009703217
## gene3 0.012275999 0.012370146 0.012193274
## gene4 0.075493239 0.076075022 0.074982142
## gene5 0.047508046 0.047884242 0.047177065
## 9995 more rows ...## An object of class "Balli"
## $Result
## log2FC_GroupB lLLI lBALLI pLLI pBALLI BCF
## gene1 0.2862964 0.3791686 0.3370391 0.5380485 0.5615433 0.1249988
## gene2 0.4147916 1.3275686 1.1800625 0.2492382 0.2773433 0.1249985
## gene3 -0.0970422 0.1338105 0.1189427 0.7145134 0.7301840 0.1249997
## gene4 -0.1410062 0.2757014 0.2450759 0.5995326 0.6205638 0.1249635
## gene5 -0.1870295 0.5887311 0.5233292 0.4429102 0.4694250 0.1249728
## 9995 more rows ...
##
## $topGenes
## log2FC_GroupB pLLI pBALLI adjpLLI adjpBALLI
## gene6890 -0.8492182 1.197256e-05 3.663179e-05 0.1121975 0.3204511
## gene7128 0.9149524 2.243950e-05 6.409023e-05 0.1121975 0.3204511
## gene3277 -1.2590606 8.539749e-05 2.121336e-04 0.2846583 0.6766089
## gene7092 -1.5531435 1.637869e-04 3.799606e-04 0.3555333 0.6766089
## gene4606 1.4661759 2.563216e-04 5.668673e-04 0.3555333 0.6766089
## 9995 more rows ...These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.