Sorry, I should have replied directly to this. My reply is below.
I am trying to run the differential methylation portion of RnBeads with pairwise.all set. It seems to recognize the pairwise option but I can't find any trace of pairwise data/tables, the group analysis completed however.
diffmeth=rnb.execute.computeDiffMeth(rnb.set,cmp.cols,region.types=reg.types)
2016-04-01 13:30:11 1.4 STATUS STARTED Retrieving comparison info
2016-04-01 13:30:11 1.4 INFO Conducting all pairwise comparisons for columns: Sample_Group
2016-04-01 13:30:11 1.4 INFO All pairwise comparisons are performed on a subset of sample groupings. Caution: this could increase the runtime significantly
due to combinatorial explosion.
2016-04-01 13:30:11 1.4 STATUS COMPLETED Retrieving comparison info
2016-04-01 13:30:11 1.4 STATUS STARTED Computing differential methylation tables
2016-04-01 13:30:11 1.4 STATUS STARTED Comparing: case vs. control (based on Sample_Group)
2016-04-01 13:30:11 1.4 STATUS STARTED Computing Differential Methylation Table (Site Level)
2016-04-01 13:30:11 1.4 INFO Conducting differential analysis using limma
2016-04-01 13:31:11 1.4 INFO 2 p-values are NA. They are treated as 1 in FDR adjustment
2016-04-01 13:31:12 1.4 STATUS COMPLETED Computing Differential Methylation Table (Site Level)
2016-04-01 13:31:13 1.6 STATUS STARTED Computing Differential Methylation Tables (Region Level)
2016-04-01 13:32:07 1.4 STATUS Computed table for genes
2016-04-01 13:32:59 1.4 STATUS Computed table for promoters
2016-04-01 13:32:59 1.4 STATUS COMPLETED Computing Differential Methylation Tables (Region Level)
2016-04-01 13:32:59 1.4 STATUS COMPLETED Comparing: case vs. control (based on Sample_Group)
2016-04-01 13:32:59 1.4 STATUS COMPLETED Computing differential methylation tables
Warning message:
Partial NA coefficients for 2 probe(s)
get.comparisons(diffmeth)
cmp1
"case vs. control (based on Sample_Group)"
get.comparison.grouplabels(diffmeth)
[,1] [,2]
case vs. control (based on Sample_Group) "case" "control"
comp = get.comparisons(diffmeth)[1]
genes = get.table(diffmeth,comp,"genes",return.data.frame=TRUE)
head(genes)
mean.mean.g1 mean.mean.g2 mean.mean.diff mean.mean.quot.log2 comb.p.val
1 0.3319096 0.3327638 -0.0008541687 -0.04855929 0.74694255
2 0.8598768 0.8697039 -0.0098270964 -0.01620692 0.63655875
3 0.8392197 0.8507916 -0.0115719742 -0.01952629 0.62718633
4 0.8392197 0.8507916 -0.0115719742 -0.01952629 0.62718633
5 0.5970268 0.7803864 -0.1833596375 -0.38079799 0.04878414
6 0.8561410 0.8207098 0.0354311214 0.06025719 0.18745396
comb.p.adj.fdr num.sites mean.num.na.g1 mean.num.na.g2 mean.mean.covg.g1
1 0.8191552 5 0 0 12.3
2 0.8051783 1 0 0 16.0
3 0.8051783 1 0 0 22.5
4 0.8051783 1 0 0 22.5
5 0.8051783 1 0 0 13.5
6 0.8051783 1 0 0 13.5
mean.mean.covg.g2 mean.nsamples.covg.thresh.g1 mean.nsamples.covg.thresh.g2
1 12.3 2 2
2 12.5 2 2
3 15.0 2 2
4 15.0 2 2
5 10.5 2 2
6 15.0 2 2
I even tried running rnb.run.differential() and the same thing, just the groups difference tables are created. How can I get the pairwise data?
Marcus
2 answers
Hi Marcus, Not sure I understand correctly. Could you clarify what you mean pairwise data? The "differential.comparison.columns.all.pairwise" option in RnBeads is to tell it to compare all pairs of groups in a given annotation column (in contrast to the default where it does one-vs-all comparisons). In your case you only seem to have two groups, so it would not make a difference.
Best, Fabian
I miss understood that. By pairwise I mean a pair of samples not pair of groups, so strictly 1 case vs 1 control. In my case an affected child vs an unaffected mother. When I tried to put just two samples in the sample_annotation file it does not run at all, it would not run with three samples either (1 case vs 2 controls, the two parents), when there is < 2 of either case or control at the 'Retrieving comparison info' part it fails. Ultimately, I have 3 cases and 3 controls (mothers), or 6 controls (mothers+fathers), but I am not completely sure that all 3 cases are of the same phenotype so I was trying to find a quick way of doing the 3 cases vs their 3 controls separately as pairs of samples, or separately as family trios, to supplement the original analysis of group 1 vs group 2, with the 3 cases and controls in their respective groups.
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