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 58 14 83 70 27 46 78 70 68 28 93 79 56 59 100 94 93 67 64 34
## gene2 28 21 22 18 30 24 13 6 29 22 40 22 26 25 20 18 21 24 27 15
## gene3 24 79 46 63 20 163 18 2 75 71 42 77 191 9 42 66 167 14 94 18
## gene4 77 61 65 74 115 83 73 71 80 71 92 50 83 107 59 153 60 86 124 90
## gene5 9 61 53 80 51 44 47 112 40 35 54 34 55 142 64 46 103 60 67 33
## gene6 15 33 30 19 25 9 13 43 19 12 13 35 31 22 28 13 31 35 40 48## [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## calcNormFactors has been renamed to normLibSizes## 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 58 14 83 70 27 46 78 70 68 28 93 79 56 59 100 94 93 67 64 34
## gene2 28 21 22 18 30 24 13 6 29 22 40 22 26 25 20 18 21 24 27 15
## gene3 24 79 46 63 20 163 18 2 75 71 42 77 191 9 42 66 167 14 94 18
## gene4 77 61 65 74 115 83 73 71 80 71 92 50 83 107 59 153 60 86 124 90
## gene5 9 61 53 80 51 44 47 112 40 35 54 34 55 142 64 46 103 60 67 33
## 9995 more rows ...
##
## $samples
## group lib.size norm.factors
## A1 A 548248 1.0010724
## A2 A 550389 0.9947607
## A3 A 552285 0.9858546
## A4 A 556162 0.9994703
## A5 A 555388 0.9997659
## 15 more rows ...## An object of class "TecVarList"
## $targets
## group lib.size norm.factors
## A1 A 548835.9 1.0010724
## A2 A 547505.4 0.9947607
## A3 A 544472.7 0.9858546
## A4 A 555867.4 0.9994703
## A5 A 555258.0 0.9997659
## 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 6.772248 4.867913 7.286049 7.017459 5.707516 6.453152 7.185150 7.036412
## gene2 5.772062 5.391819 5.460604 5.170389 5.849462 5.568328 4.769751 3.865000
## gene3 5.565554 7.208671 6.461312 6.869940 5.309213 8.234765 5.184900 2.863324
## gene4 7.169183 6.846037 6.942640 7.095442 7.719316 7.277736 7.092035 7.056314
## gene5 4.323962 6.846037 6.657800 7.205042 6.577104 6.391736 6.477879 7.699446
## A9 A10 B1 B2 B3 B4 B5 B6
## gene1 6.979869 5.757804 7.438111 7.197857 6.695832 6.792289 7.540640 7.433448
## gene2 5.805131 5.436038 6.260349 5.443087 5.646058 5.616371 5.327048 5.171345
## gene3 7.117347 7.040356 6.327481 7.161789 8.429751 4.320739 6.327429 6.936049
## gene4 7.208098 7.040356 7.422843 6.558474 7.246989 7.629759 6.798841 8.124516
## gene5 6.243088 6.060244 6.675487 6.027994 6.670755 8.031507 6.912519 6.433693
## B7 B8 B9 B10
## gene1 7.434660 6.962817 6.913274 6.033218
## gene2 5.388041 5.555013 5.726507 4.950538
## gene3 8.265729 4.854854 7.453929 5.185065
## gene4 6.818945 7.313687 7.846292 7.386974
## gene5 7.579060 6.808508 6.977417 5.992571
## 9995 more rows ...
##
## $tecVar
## A1 A2 A3 A4 A5 A6
## gene1 0.01854310 0.01858751 0.01868955 0.01831192 0.01833171 0.01858118
## gene2 0.04352061 0.04362221 0.04385495 0.04299159 0.04303692 0.04360773
## gene3 0.02409259 0.02414998 0.02428180 0.02379386 0.02381946 0.02414180
## gene4 0.01193813 0.01196507 0.01202699 0.01179779 0.01180982 0.01196124
## gene5 0.01937043 0.01941683 0.01952342 0.01912891 0.01914960 0.01941022
## A7 A8 A9 A10 B1 B2
## gene1 0.01851478 0.01855742 0.01835111 0.01835055 0.01278865 0.01270212
## gene2 0.04345583 0.04355339 0.04308137 0.04308009 0.03756484 0.03730079
## gene3 0.02405601 0.02411110 0.02384455 0.02384383 0.01852087 0.01838878
## gene4 0.01192095 0.01194683 0.01182162 0.01182128 0.01070383 0.01063164
## gene5 0.01934085 0.01938540 0.01916989 0.01916930 0.01497876 0.01487390
## B3 B4 B5 B6 B7 B8
## gene1 0.01253218 0.01273218 0.01278820 0.01262285 0.01275947 0.01267078
## gene2 0.03677946 0.03739253 0.03756345 0.03705753 0.03747578 0.03720461
## gene3 0.01812833 0.01843467 0.01852018 0.01826721 0.01847632 0.01834068
## gene4 0.01048905 0.01065672 0.01070345 0.01056512 0.01067948 0.01060534
## gene5 0.01466759 0.01491033 0.01497821 0.01477729 0.01494340 0.01483570
## B9 B10
## gene1 0.01279465 0.01274774
## gene2 0.03758313 0.03743998
## gene3 0.01853004 0.01845841
## gene4 0.01070884 0.01066970
## gene5 0.01498604 0.01492918
## 9995 more rows ...## An object of class "Balli"
## $Result
## log2FC_GroupB lLLI lBALLI pLLI pBALLI BCF
## gene1 0.5378744 3.4438555 3.0612346 0.06348738 0.08018072 0.1249891
## gene2 0.1993370 0.8987859 0.7989554 0.34310747 0.37140586 0.1249512
## gene3 0.3407542 0.3017664 0.2682369 0.58277717 0.60451692 0.1249994
## gene4 0.1699873 1.0559371 0.9386153 0.30414367 0.33263420 0.1249945
## gene5 0.3626869 1.2020167 1.0684633 0.27291897 0.30129281 0.1249958
## 9995 more rows ...
##
## $topGenes
## log2FC_GroupB pLLI pBALLI adjpLLI adjpBALLI
## gene3444 0.8595940 1.782716e-06 6.683598e-06 0.01782716 0.06683598
## gene6069 0.7652805 3.331310e-05 9.142174e-05 0.11908089 0.32412302
## gene5565 0.8740074 3.572427e-05 9.723691e-05 0.11908089 0.32412302
## gene7401 0.9920952 7.322260e-05 1.848236e-04 0.18305650 0.46205891
## gene4554 -0.6330306 9.835354e-05 2.407207e-04 0.19670709 0.48144148
## 9995 more rows ...