f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome.

Abstract:

:The ability to integrate 'omics' (i.e. transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs) across different 'omics' data types or multi-dimensional data including time courses. We present fCI (f-divergence Cut-out Index), a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that fCI can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying fCI to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package fCI is available at R Bioconductor and http://software.steenlab.org/fCI/.

journal_name

Nucleic Acids Res

journal_title

Nucleic acids research

authors

Tang S,Hemberg M,Cansizoglu E,Belin S,Kosik K,Kreiman G,Steen H,Steen J

doi

10.1093/nar/gkw157

subject

Has Abstract

pub_date

2016-06-02 00:00:00

pages

e97

issue

10

eissn

0305-1048

issn

1362-4962

pii

gkw157

journal_volume

44

pub_type

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