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 27 28 76 35 65 16 44 25 26 29 22 46 30 20 23 29 49 32 35 54
## gene2 9 49 42 46 15 21 27 26 82 23 38 42 70 17 30 30 24 99 44 17
## gene3 6 5 7 11 12 15 6 16 28 24 28 15 6 8 26 5 6 6 16 16
## gene4 27 28 26 23 55 51 77 34 31 17 11 36 19 19 24 18 20 25 46 34
## gene5 74 139 99 129 109 83 62 76 131 119 101 58 75 70 122 135 92 236 72 67
## gene6 81 80 62 77 53 67 62 67 88 53 95 56 54 45 62 59 92 85 56 71## [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 27 28 76 35 65 16 44 25 26 29 22 46 30 20 23 29 49 32 35 54
## gene2 9 49 42 46 15 21 27 26 82 23 38 42 70 17 30 30 24 99 44 17
## gene3 6 5 7 11 12 15 6 16 28 24 28 15 6 8 26 5 6 6 16 16
## gene4 27 28 26 23 55 51 77 34 31 17 11 36 19 19 24 18 20 25 46 34
## gene5 74 139 99 129 109 83 62 76 131 119 101 58 75 70 122 135 92 236 72 67
## 9995 more rows ...
##
## $samples
## group lib.size norm.factors
## A1 A 548524 1.0007690
## A2 A 550012 0.9888917
## A3 A 542979 0.9995316
## A4 A 552839 0.9997865
## A5 A 550961 0.9946588
## 15 more rows ...## An object of class "TecVarList"
## $targets
## group lib.size norm.factors
## A1 A 548945.8 1.0007690
## A2 A 543902.3 0.9888917
## A3 A 542724.7 0.9995316
## A4 A 552721.0 0.9997865
## A5 A 548018.2 0.9946588
## 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 5.723203 5.784547 7.166668 6.065331 6.933697 5.038205 6.388529 5.608825
## gene2 4.324603 6.550458 6.340365 6.440723 4.954816 5.391937 5.722943 5.661261
## gene3 3.865140 3.682003 4.047884 4.557261 4.674635 4.955718 3.864930 5.024299
## gene4 5.723203 5.784547 5.687851 5.499979 6.700482 6.596488 7.168751 6.023645
## gene5 7.113169 8.017927 7.539576 7.888934 7.662058 7.278014 6.864969 7.138770
## A9 A10 B1 B2 B3 B4 B5 B6
## gene1 5.670375 5.802322 5.440357 6.435165 5.871322 5.325389 5.511329 5.835695
## gene2 7.255246 5.492259 6.176980 6.309692 7.041488 5.113867 5.867524 5.881535
## gene3 5.769902 5.548788 5.762114 4.938828 3.870019 4.187747 5.674853 3.684922
## gene4 5.907393 5.096783 4.556560 6.098298 5.263412 5.258270 5.567921 5.202797
## gene5 7.918194 7.766128 7.541237 6.756965 7.138362 7.035963 7.821855 7.980417
## B7 B8 B9 B10
## gene1 6.539904 5.949126 6.079063 6.667703
## gene2 5.567814 7.519744 6.393225 5.108597
## gene3 3.866891 3.862300 5.039245 5.030622
## gene4 5.326766 5.616601 6.454635 6.030365
## gene5 7.422118 8.756313 7.079200 6.968842
## 9995 more rows ...
##
## $tecVar
## A1 A2 A3 A4 A5 A6
## gene1 0.026108547 0.026346400 0.026402438 0.025933312 0.026151972 0.026170167
## gene2 0.030345774 0.030619127 0.030683672 0.030144376 0.030395679 0.030416589
## gene3 0.072067605 0.072676052 0.072819649 0.071619006 0.072178726 0.072225278
## gene4 0.026089059 0.026326750 0.026382778 0.025913955 0.026132452 0.026150633
## gene5 0.009334899 0.009415628 0.009434681 0.009275396 0.009349642 0.009355818
## A7 A8 A9 A10 B1 B2
## gene1 0.026103576 0.025892702 0.026066426 0.025785404 0.026861906 0.026771306
## gene2 0.030340061 0.030097700 0.030297366 0.029974246 0.024523873 0.024440707
## gene3 0.072054886 0.071515001 0.071959806 0.071240122 0.072137799 0.071911649
## gene4 0.026084092 0.025873375 0.026046970 0.025766157 0.036467254 0.036346156
## gene5 0.009333212 0.009261604 0.009320597 0.009225155 0.009703084 0.009671963
## B3 B4 B5 B6 B7 B8
## gene1 0.027178012 0.027073293 0.027103321 0.027379455 0.027101168 0.026988683
## gene2 0.024813822 0.024717816 0.024745346 0.024998835 0.024743372 0.024640247
## gene3 0.072926196 0.072665149 0.072740029 0.073428800 0.072734661 0.072454122
## gene4 0.036889157 0.036749482 0.036789535 0.037158283 0.036786664 0.036636620
## gene5 0.009811592 0.009775658 0.009785964 0.009880801 0.009785225 0.009746616
## B9 B10
## gene1 0.027143474 0.026965157
## gene2 0.024782158 0.024618653
## gene3 0.072840137 0.072395432
## gene4 0.036843092 0.036605237
## gene5 0.009799742 0.009738539
## 9995 more rows ...## An object of class "Balli"
## $Result
## log2FC_GroupB lLLI lBALLI pLLI pBALLI BCF
## gene1 -0.052465577 0.0481389525 0.0427902063 0.82633387 0.83612081 0.1249993
## gene2 0.284639814 0.6680254769 0.5938034377 0.41374086 0.44095211 0.1249943
## gene3 -0.007381279 0.0005223832 0.0004643406 0.98176537 0.98280806 0.1250001
## gene4 -0.481212196 3.4177582467 3.0381623040 0.06449854 0.08132794 0.1249426
## gene5 -0.068676980 0.1006934258 0.0895052854 0.75099908 0.76480703 0.1249998
## 9995 more rows ...
##
## $topGenes
## log2FC_GroupB pLLI pBALLI adjpLLI adjpBALLI
## gene3410 2.0382397 1.093056e-05 3.376546e-05 0.1093056 0.3376546
## gene5607 1.0290866 1.003620e-04 2.449344e-04 0.4435383 0.8134918
## gene318 -0.8030151 1.712952e-04 3.952470e-04 0.4435383 0.8134918
## gene637 -1.2381053 2.703061e-04 5.944122e-04 0.4435383 0.8134918
## gene9750 -0.9521504 3.043120e-04 6.614722e-04 0.4435383 0.8134918
## 9995 more rows ...