Intro_Functions.RmdThis package provides functions for nine depth normalization methods and for two differential expression analysis methods.
Among the nine normalization methods, six are re-scaling-based and three are regression-based. The input for each normalization function includes (1) sequencing count data in the format of a data frame or a data matrix (with columns for samples and rows for miRNAs/markers) and (2) the sample groups in the form of a character vector. For re-scaling-based methods, the output is a matrix of the normalized data and a vector of the estimated sample-specific scaling factors. For regression-based methods, the output is a matrix of the un-normalized data and a vector of the estimated adjustment factors.
test.TC <- norm.TC(data.test, data.group)
test.UQ <- norm.UQ(data.test, data.group)
test.med <- norm.med(data.test, data.group)
test.TMM <- norm.TMM(data.test, data.group)
test.DESeq <- norm.DESeq(data.test, data.group)
test.PoissonSeq <- norm.PoissonSeq(data.test)
test.QN <- norm.QN(data.test)
test.SVA <- norm.SVA(data.test, data.group)
test.RUVg <- norm.RUVg(data.test, data.group)
test.RUVs <- norm.RUVs(data.test, data.group)
test.RUVr <- norm.RUVr(data.test, data.group)
head(test.TMM$scaling.factor)
#> [1] 1.1988963 0.4295173 1.2377190 1.3487078 1.1959610 1.8503994
head(test.TMM$dat.normed[,1:5])
#> MXF2516 MXF742 MXF772 MXF832 MXF836
#> hsa-let-7a-2* 0.00000 0.00000 0.00000 1.482901 1.672295
#> hsa-let-7a(3) 70709.20148 14821.28856 48696.83516 53595.004767 39640.925166
#> hsa-let-7a*(2) 2829.26889 34.92292 126.84623 269.146516 102.846164
#> hsa-let-7b 7376.78480 10437.29762 20702.59825 20564.869850 14440.270362
#> hsa-let-7b* 24.18891 32.59473 58.97946 83.042451 71.072552
#> hsa-let-7c 23107.92019 1008.10838 13457.01204 5153.822120 11593.187526
head(test.SVA$adjust.factor)
#> [,1]
#> [1,] 0.28535304
#> [2,] 0.03121055
#> [3,] -0.07462108
#> [4,] -0.06139403
#> [5,] -0.07546172
#> [6,] -0.12748833
head(test.SVA$dat.normed[,1:5])
#> MXF2516 MXF742 MXF772 MXF832
#> hsa-let-7a-2* 3.126439e+00 0.3419549 -0.8175775 1.327343
#> hsa-let-7a(3) 1.143937e+05 9605.7728904 52527.0508087 65911.068255
#> hsa-let-7a*(2) 3.041342e+03 -23.3533362 248.6987096 438.444546
#> hsa-let-7b 2.310206e+04 6042.4781503 21895.4551525 24668.362809
#> hsa-let-7b* 6.650161e+01 18.1017473 63.1931625 103.931485
#> hsa-let-7c 3.792122e+04 1550.5108226 13984.1515878 4752.753259
#> MXF836
#> hsa-let-7a-2* 1.173212
#> hsa-let-7a(3) 39575.789137
#> hsa-let-7a*(2) 215.731738
#> hsa-let-7b 13499.451384
#> hsa-let-7b* 75.082684
#> hsa-let-7c 11163.051990Two methods for assessing differential expression are edgeR and voom-limma. The input can be either the normalized dataset (from re-scaling normalization) or the un-normalized dataset plus adjustment factors (from regression-based normalization). The output includes the per-marker p-values, and the group mean differences, and the names of the significant markers (using a p-value cutoff specified by the user).
edgeR.benchmark <- DE.edgeR(data.benchmark, data.group)
voom.benchmark <- DE.voom(data.benchmark, data.group)
edgeR.test.TMM <- DE.edgeR(test.TMM$dat.normed, data.group)
voom.test.TMM <- DE.voom(test.TMM$dat.normed, data.group)
edgeR.test.RUVr <- DE.edgeR(data.test, data.group,
normalized = FALSE,
adjust = test.RUVr$adjust.factor)
voom.test.RUVr <- DE.voom(data.test, data.group,
normalized = FALSE,
adjust = test.RUVr$adjust.factor)
head(voom.benchmark$id.list)
#> [1] "hsa-miR-99a*" "hsa-miR-26a-2*" "hsa-miR-146b" "hsa-miR-7(3)"
#> [5] "hsa-miR-138(2)" "hsa-miR-138-1*"
head(voom.benchmark$p.val)
#> hsa-miR-99a* hsa-miR-26a-2* hsa-miR-146b hsa-miR-7(3) hsa-miR-138(2)
#> 2.020888e-05 4.215158e-05 7.168172e-05 7.320699e-05 8.713003e-05
#> hsa-miR-138-1*
#> 1.607036e-04
head(voom.test.RUVr$id.list)
#> [1] "hsa-miR-125b(2)" "hsa-miR-99b" "hsa-miR-99a*" "hsa-miR-99a"
#> [5] "hsa-miR-125a" "hsa-miR-146b"
head(voom.test.RUVr$p.val)
#> hsa-miR-125b(2) hsa-miR-99b hsa-miR-99a* hsa-miR-99a hsa-miR-125a
#> 1.138177e-07 1.177162e-07 3.501843e-07 6.603158e-07 2.243507e-06
#> hsa-miR-146b
#> 3.071453e-06A function is included in the package for connecting the step of depth normalization and the step of differential expression analysis.
res.norm <- pip.norm(data.test, data.group, "norm.TMM")
res.DE <- pip.norm.DE(data.test, data.group, "norm.TMM")Some normalization methods, such as the SVA method, compute “adjustment” instead of normalized counts. For illustration, we repeat the PRECISION.seq analysis using SVA.
# Compute normalization
sva.norm <- norm.SVA(data.test, data.group)
# sva.norm contains the
res.sva <- precision.seq(sva.norm$dat.normed,
method.name="sva",
adjust.factors=sva.norm$adjust.factor,
DE.method="DE.voom",
Pval=0.01)