The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
If you encounter dependency error for sva package, install it from Bioconductor:
omicwas is a package for cell-type-specific disease association testing, using bulk tissue data as input. The package accepts DNA methylation data for epigenome-wide association studies (EWAS), as well as gene expression data for differential gene expression analyses. The main function is ctassoc
See description.
Let’s load a sample data.
data(GSE42861small)
X = GSE42861small$X
W = GSE42861small$W
Y = GSE42861small$Y
Y = Y[seq(1, 20), ] # for brevity
C = GSE42861small$CSee description.
The conventional way is to use ordinary linear regression.
result = ctassoc(X, W, Y, C = C,
test = "full")
#> Linear regression ...
#> Summarizing result ...
result$coefficients
#> # A tibble: 380 x 6
#> response celltype term estimate statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 cg10543797 CD4. disease 0.0116 0.387 6.99e- 1
#> 2 cg10543797 CD4. 1 0.818 54.1 8.77e-241
#> 3 cg10543797 CD8. disease 0.0683 2.21 2.71e- 2
#> 4 cg10543797 CD8. 1 0.798 51.0 1.45e-227
#> 5 cg10543797 mono disease -0.0611 -0.977 3.29e- 1
#> 6 cg10543797 mono 1 0.745 23.5 2.27e- 88
#> 7 cg10543797 Bcells disease 0.0469 0.761 4.47e- 1
#> 8 cg10543797 Bcells 1 0.868 28.2 4.48e-114
#> 9 cg10543797 NK disease -0.0272 -0.764 4.45e- 1
#> 10 cg10543797 NK 1 0.817 43.6 7.22e-194
#> # … with 370 more rowsWe recommend nonlinear regression with ridge regularization. For DNA methylation, we use the logit function for normalization, and the test option is nls.logit
result = ctassoc(X, W, Y, C = C,
test = "nls.logit",
regularize = TRUE)
#> nls.logit ...
#>
|
| | 0%
|
|======================================================================| 100%
#> Summarizing result ...
print(result$coefficients, n = 20)
#> # A tibble: 380 x 6
#> response celltype term estimate statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 cg10543797 CD4. 1 1.50 15.5 1.24e- 46
#> 2 cg10543797 CD8. 1 1.33 14.5 9.53e- 42
#> 3 cg10543797 mono 1 1.19 6.93 1.03e- 11
#> 4 cg10543797 Bcells 1 1.81 7.29 9.12e- 13
#> 5 cg10543797 NK 1 1.63 13.5 8.63e- 37
#> 6 cg10543797 Nue 1 1.35 56.3 1.32e-253
#> 7 cg10543797 Eos 1 0.996 5.84 8.30e- 9
#> 8 cg10543797 CD4. disease 0.000711 0.465 6.42e- 1
#> 9 cg10543797 CD8. disease 0.00202 1.43 1.52e- 1
#> 10 cg10543797 mono disease -0.000917 -0.921 3.57e- 1
#> 11 cg10543797 Bcells disease 0.000350 0.505 6.14e- 1
#> 12 cg10543797 NK disease -0.000504 -0.468 6.40e- 1
#> 13 cg10543797 Nue disease -0.00555 -0.855 3.93e- 1
#> 14 cg10543797 Eos disease 0.000278 0.474 6.35e- 1
#> 15 cg10543797 <NA> sexF -0.0415 -3.94 9.16e- 5
#> 16 cg10543797 <NA> age -0.000303 -0.746 4.56e- 1
#> 17 cg10543797 <NA> smoking_current -0.00532 -0.430 6.67e- 1
#> 18 cg10543797 <NA> smoking_ex -0.0192 -1.58 1.13e- 1
#> 19 cg10543797 <NA> smoking_occasional 0.00109 0.0627 9.50e- 1
#> 20 cg22718169 CD4. 1 2.18 15.5 2.75e- 46
#> # … with 360 more rowsThe first 19 lines show the result for CpG site cg10543797. Line 1 shows that the basal methylation level in CD4. (actually CD4+ T cells) is plogis(1.498) = 0.817, so this cell type is 81% methylated. Line 8 shows that the CD4.-specific effect of the disease is 7.10e-04 (in logit scale). Since the p.value is 0.64, this is not significant. Line 15 shows that the effect of sexF (female compared to male) is -4.14e-02 with a small p.value 9.15e-05. Since sexF is a covariate that has uniform effect across cell types, the celltype column is NA.
Let’s load a sample data.
data(GTExsmall)
X = GTExsmall$X
W = GTExsmall$W
Y = GTExsmall$Y + 1
Y = Y[seq(1, 20), ] # for brevity
C = GTExsmall$CSee description.
The conventional way is to use ordinary linear regression.
result = ctassoc(X, W, Y, C = C,
test = "full")
#> Linear regression ...
#> Summarizing result ...
result$coefficients
#> # A tibble: 260 x 6
#> response celltype term estimate statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 ENSG00000059804 Granulocytes age 45611. 2.56 1.07e- 2
#> 2 ENSG00000059804 Granulocytes 1 378100. 1.64 1.02e- 1
#> 3 ENSG00000059804 B.cells..CD19.. age 143773. 0.683 4.95e- 1
#> 4 ENSG00000059804 B.cells..CD19.. 1 -774328. -0.324 7.46e- 1
#> 5 ENSG00000059804 CD4..T.cells age -104302. -2.42 1.59e- 2
#> 6 ENSG00000059804 CD4..T.cells 1 -1043816. -2.28 2.30e- 2
#> 7 ENSG00000059804 CD8..T.cells age 94351. 1.42 1.57e- 1
#> 8 ENSG00000059804 CD8..T.cells 1 2577296. 3.49 5.47e- 4
#> 9 ENSG00000059804 NK.cells..CD3..CD56.. age 134268. 2.34 1.96e- 2
#> 10 ENSG00000059804 NK.cells..CD3..CD56.. 1 5027033. 6.61 1.33e-10
#> # … with 250 more rowsWe recommend nonlinear regression with ridge regularization. For DNA methylation, we use the log function for normalization, and the test option is nls.log
result = ctassoc(X, W, Y, C = C,
test = "nls.log",
regularize = TRUE)
#> nls.log ...
#>
|
| | 0%
|
|======================================================================| 100%
#> Summarizing result ...
print(result$coefficients, n = 15)
#> # A tibble: 260 x 6
#> response celltype term estimate statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 ENSG00000059804 Granulocytes 1 8.85 0.544 5.87e- 1
#> 2 ENSG00000059804 B.cells..CD19.. 1 8.85 0.0490 9.61e- 1
#> 3 ENSG00000059804 CD4..T.cells 1 8.85 0.252 8.01e- 1
#> 4 ENSG00000059804 CD8..T.cells 1 10.7 1.21 2.25e- 1
#> 5 ENSG00000059804 NK.cells..CD3..CD56.. 1 14.2 52.7 2.61e-179
#> 6 ENSG00000059804 Monocytes..CD14.. 1 8.85 0.0666 9.47e- 1
#> 7 ENSG00000059804 Granulocytes age 0.000462 0.221 8.25e- 1
#> 8 ENSG00000059804 B.cells..CD19.. age 0.0000111 0.107 9.15e- 1
#> 9 ENSG00000059804 CD4..T.cells age -0.0000308 -0.0401 9.68e- 1
#> 10 ENSG00000059804 CD8..T.cells age 0.000655 0.312 7.55e- 1
#> 11 ENSG00000059804 NK.cells..CD3..CD56.. age 0.0185 4.62 5.20e- 6
#> 12 ENSG00000059804 Monocytes..CD14.. age 0.0000263 0.0955 9.24e- 1
#> 13 ENSG00000059804 <NA> sexF -0.00257 -0.0273 9.78e- 1
#> 14 ENSG00000147454 Granulocytes 1 10.1 5.20 3.20e- 7
#> 15 ENSG00000147454 B.cells..CD19.. 1 9.25 0.197 8.44e- 1
#> # … with 245 more rowsThe first 13 lines show the result for transcript ENSG00000059804. Line 1 shows that the basal expression level in Granulocytes is exp(8.847) = 6955. Line 7 shows that the Granulocytes-specific effect of age is 4.62e-04 (in log scale). Since the p.value is 0.82, this is not significant. Line 13 shows that the effect of sexF (female compared to male) is -2.57e-03 with p.value 0.97 Since sexF is a covariate that has uniform effect across cell types, the celltype column is NA.
For QTL analyses, we use ctcisQTL function instead of ctassoc. To speed up computation, we perform linear ridge regression, thus the statistical test is almost identical to ctassoc(test = "nls.identity", regularize = TRUE). We analyze only in the linear scale. Association analysis is performed between each row of Y and each row of X. See description.
Let’s load a sample data.
data(GSE79262small)
X = GSE79262small$X
Xpos = GSE79262small$Xpos
W = GSE79262small$W
Y = GSE79262small$Y
Ypos = GSE79262small$Ypos
C = GSE79262small$C
X = X[seq(1, 3001, 100), ] # for brevity
Xpos = Xpos[seq(1, 3001, 100)]
Y = Y[seq(1, 501, 100), ]
Ypos = Ypos[seq(1, 501, 100)]See description.
Analyze mQTL.
ctcisQTL(X, Xpos, W, Y, Ypos, C = C)
#> Writing output to /var/folders/7q/n5cckft14d9345z_tdpy3qqc0000gn/T//RtmpFBI9hb/ctcisQTL.out.txt
#>
|
| | 0%
|
|== | 3%
|
|===== | 6%
|
|======= | 10%
|
|========= | 13%
|
|=========== | 16%
|
|============== | 19%
|
|================ | 23%
|
|================== | 26%
|
|==================== | 29%
|
|======================= | 32%
|
|================================ | 45%
|
|================================== | 48%
|
|==================================== | 52%
|
|====================================== | 55%
|
|========================================= | 58%
|
|=========================================================== | 84%
|
|============================================================= | 87%
|
|=============================================================== | 90%
|
|================================================================= | 94%
|
|==================================================================== | 97%
|
|======================================================================| 100%The result is stored in a file.
head(
read.table(file.path(tempdir(), "ctcisQTL.out.txt"),
header = TRUE,
sep ="\t"))
#> term response celltype estimate statistic p.value
#> 1 rs6678176 cg19251656 CD4T -0.0031975545 -0.3112062 0.75693773
#> 2 rs6678176 cg19251656 CD8T 0.0029236713 0.4368269 0.66411762
#> 3 rs6678176 cg19251656 NK 0.0021900746 0.6992125 0.48765929
#> 4 rs6678176 cg01433766 CD4T -0.0007853244 -0.4642848 0.64445898
#> 5 rs6678176 cg01433766 CD8T -0.0006336713 -0.5359356 0.59437935
#> 6 rs6678176 cg01433766 NK -0.0012465110 -1.9903623 0.05203197The first 3 lines show the result for the association of SNP rs6678176 with CpG site cg19251656. Line 1 shows that the CD4T-specific effect of the SNP is -0.003. Since the p.value is 0.75, this is not significant.
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.