Removing Confounding Factor Effects using SVA

Removing Confounding Factor Effects using SVA

cfr_sva(mat, age, cov = NA)

Arguments

mat	a numeric matrix with the expression values, where columns are the samples and rows are probesets, transcripts, or genes.
age	a numeric vector, where the names correspond to samples (the same as colnames of the given matrix).
cov	covariates to analyse SVs.

Value

a list with i) a numeric matrix with the expression values, ii) SVs for each sample, and iii) correlation between SVs and covariates

Examples

sampnames = paste('sample',1:20,sep='')
myexp <- sapply( 1:20, function(i){ rnorm(n = 10000, mean = sample(1:3, 1), sd = sample(c(1, 3), 1)) })
colnames(myexp) = sampnames
agevec <- sample(20:80,20)
names(agevec) = sampnames
result_mat <- cfr_sva(myexp,agevec)
#> Number of significant surrogate variables is:  8 
#> Iteration (out of 5 ):1  2  3  4  5  
print(myexp[1:5,1:5])
#>        sample1  sample2    sample3  sample4      sample5
#> [1,] 1.6344855 2.152249  0.9867031 2.792520 -0.005623023
#> [2,] 0.8613537 1.892670 -1.0850652 2.221501  3.618923469
#> [3,] 1.9471171 2.165830  1.4713725 3.014678  3.388419345
#> [4,] 1.6430524 3.431929  1.0257522 2.897891  1.571431039
#> [5,] 1.3218842 2.877333  0.2430339 2.741432  5.385300746
print(result_mat$correctedExp[1:5,1:5])
#>         sample1     sample2     sample3    sample4      sample5
#> [1,] -0.3679575  0.10686609 -0.03129762 -0.2271763 -0.014059469
#> [2,] -1.0878495 -0.04840652 -1.62329177 -1.0601329  0.016051265
#> [3,] -0.7078149 -0.46955222  0.27452364 -1.0419228 -0.026707695
#> [4,] -0.2078332  1.54909703 -0.24695464  0.4203431 -0.005673507
#> [5,] -0.9584203  0.61369261 -0.98971666 -0.5536472 -0.006003883
dim(result_mat$correctedExp)
#> [1] 10000    20
head(result_mat$SVs)
#>                SV_1          SV_2         SV_3          SV_4          SV_5
#> sample1  0.01852815 -3.339818e-03  0.005042591  0.0100746681 -0.0060938556
#> sample2  0.01638531 -2.279730e-06  0.009879715  0.0012308819 -0.0005983815
#> sample3 -0.18678370 -3.979141e-03  0.010207727  0.0004481191 -0.0061359900
#> sample4  0.22052090 -7.401643e-03  0.006720401  0.0084531081  0.0065031948
#> sample5  0.03912771 -1.516710e-01 -0.076601154 -0.3557168208  0.5687845492
#> sample6  0.04714716  5.161590e-01  0.694451702 -0.2609277419 -0.1656813638
#>                 SV_6       SV_7         SV_8
#> sample1 -0.005255565 -0.1538935 -0.041312445
#> sample2 -0.002327802 -0.1579122 -0.031283564
#> sample3 -0.008249627 -0.1011968 -0.391975117
#> sample4  0.006643142 -0.2231104  0.313830686
#> sample5  0.615475843  0.3008639  0.006176108
#> sample6 -0.024525553  0.3186825  0.035075428
head(result_mat$SV_cov_corr)
#> NULL