In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 3 ( 2023-3-9), p. e1010342-
Kurzfassung:
The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called “differential expression analysis” approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.
Materialart:
Online-Ressource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010342
DOI:
10.1371/journal.pcbi.1010342.g001
DOI:
10.1371/journal.pcbi.1010342.g002
DOI:
10.1371/journal.pcbi.1010342.g003
DOI:
10.1371/journal.pcbi.1010342.g004
DOI:
10.1371/journal.pcbi.1010342.g005
DOI:
10.1371/journal.pcbi.1010342.g006
DOI:
10.1371/journal.pcbi.1010342.t001
DOI:
10.1371/journal.pcbi.1010342.s001
DOI:
10.1371/journal.pcbi.1010342.s002
DOI:
10.1371/journal.pcbi.1010342.s003
DOI:
10.1371/journal.pcbi.1010342.s004
DOI:
10.1371/journal.pcbi.1010342.s005
DOI:
10.1371/journal.pcbi.1010342.s006
DOI:
10.1371/journal.pcbi.1010342.s007
DOI:
10.1371/journal.pcbi.1010342.s008
DOI:
10.1371/journal.pcbi.1010342.s009
DOI:
10.1371/journal.pcbi.1010342.s010
DOI:
10.1371/journal.pcbi.1010342.s011
DOI:
10.1371/journal.pcbi.1010342.s012
DOI:
10.1371/journal.pcbi.1010342.s013
DOI:
10.1371/journal.pcbi.1010342.r001
DOI:
10.1371/journal.pcbi.1010342.r002
DOI:
10.1371/journal.pcbi.1010342.r003
DOI:
10.1371/journal.pcbi.1010342.r004
Sprache:
Englisch
Verlag:
Public Library of Science (PLoS)
Publikationsdatum:
2023
ZDB Id:
2193340-6