Dealing with very unbalanced class sizes in differential expression
I' am looking at some TCGA data and want to compare the expression profiles of tumors with a certain fusion (4 samples) vs those without it (~500 samples). I don't think current algorithms for differential expression will work with such unbalanced classes (e.g. DESeq2). It seems there may be some strategies I can take from the machine learning community (e.g. under sampling the larger class). Do you have any recommendations or can point me to any papers?
Thanks!
• 2,632 views
•
link
written
by
murphy.charlesj
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Should differential expression analysis be incorporated in cross validation for training machine le…
written by yordany.perdigon •
-
Feasibility of detecting PCR-chimeric reads with Machine Learing (ML) for organelle genome assembli…
written by moreDanOne •
-
Single MAG Recruitment to multiple metaT Samples
written by zjhennin •
-
Did TCGA consortium recommend any best practice guideline for cross-sample RNAseq comparison ?
written by CrazyB
-
Possible approach to select normal tissue samples for cancer RNA-Seq data without reference data fo…
written by svlachavas
-
Predictive models from RNA (co)expression data
written by mattia.bosio85 •
-
How to use TCGA data for cancer prediction and prognosis ?
written by Ld_60 •
-
Genome annotation tools - current status. Machine learning?
written by liorglic
-
Paired transcriptomics and lipidomics data
written by ncl.lazzarini
-
Getting BLAST Clusters Classifier performance for Protein classes
written by ddofer •
One way could be taking the mean of Expression values of all Samples(500 samples,without it), to get a single Mean Expression value per gene. And similarly take the mean of Expression values of 4 samples,and divide to get a log2foldchange for differential expression
My advisor suggested something similar, so I'll definitely give it a try. Thanks.