Deleted:converting extremely sparse count dataframe to continuous distributions for study in WGCNA
1
0
Entering edit mode
@chrisclarkson100-11114
Last seen 3 months ago
United Kingdom

I am very inexperienced with mathematics and expression data.

I have developed a pipeline for WGCNA, practicing with gene-expression microarray data. I am now determined to try to apply this strategy to microbial-communities count data. 

Initially I tried finding an adjacency matrix with the natural count-data:

adjacency(df)

And this indeed produced a set of plots- however certain WGCNA commands won't work such as 'pickSoftThreshold' won't recommend a 'powerEstimate', returning the following Warning repeated many times:

Warning in eval(expr, envir, enclos) :
  Some correlations are NA in block 1 : 790 .
Warning in as.vector(log10(dk)) : NaNs produced

So I tried using voom to convert it to the continuous dataset. This works but I am doubtful of voom's output:

voom(df, plot-T)

enter image description here

Contrasting this plot with that of a typical plot from the 'voom' paper (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29) indicates that this output is not valid- given that the data is so sparse.

How can I convert such a sparse count data frame to a validly continuous one?

The following post is related to this one but I did not understand most of the terms that were being used: voom for spectral counts

limma voom voom counts • 1.1k views
ADD COMMENT
This thread is not open. No new answers may be added
Traffic: 468 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6