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@article{1412163,
author = {Ihnatová, Ivana and Popovici, Vlad and Budinská, Eva},
article_location = {San Francisco},
article_number = {1},
doi = {http://dx.doi.org/10.1371/journal.pone.0191154},
keywords = {topology-based pathway analysis; gene-set enrichment analysis; pathway analysis; microarrays; RNA-seq},
language = {eng},
issn = {1932-6203},
journal = {Plos one},
title = {A critical comparison of topology-based pathway analysis methods},
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191154},
volume = {13},
year = {2018}
}
TY - JOUR
ID - 1412163
AU - Ihnatová, Ivana - Popovici, Vlad - Budinská, Eva
PY - 2018
TI - A critical comparison of topology-based pathway analysis methods
JF - Plos one
VL - 13
IS - 1
SP - 1-24
EP - 1-24
PB - Public Library Science
SN - 19326203
KW - topology-based pathway analysis
KW - gene-set enrichment analysis
KW - pathway analysis
KW - microarrays
KW - RNA-seq
UR - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191154
L2 - https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191154
N2 - One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis relied on enrichment-based approaches, a newer generation of methods is now available, exploiting pathway topologies in addition to gene/protein expression levels. However, little effort has been invested in their critical assessment with respect to their performance in different experimental setups. Here, we assessed the performance of seven representative methods identifying differentially expressed pathways between two groups of interest based on gene expression data with prior knowledge of pathway topologies: SPIA, PRS, CePa, TAPPA, TopologyGSA, Clipper and DEGraph. We performed a number of controlled experiments that investigated their sensitivity to sample and pathway size, threshold-based filtering of differentially expressed genes, ability to detect target pathways, ability to exploit the topological information and the sensitivity to different preprocessing strategies. We also verified type I error rates and described the influence of overexpression of single genes, gene sets and topological motifs of various sizes on the detection of a pathway as differentially expressed. The results of our experiments demonstrate a wide variability of the tested methods. We provide a set of recommendations for an informed selection of the proper method for a given data analysis task.
ER -
IHNATOVÁ, Ivana, Vlad POPOVICI and Eva BUDINSKÁ. A critical comparison of topology-based pathway analysis methods. \textit{Plos one}. San Francisco: Public Library Science, 2018, vol.~13, No~1, p.~1-24. ISSN~1932-6203. Available from: https://dx.doi.org/10.1371/journal.pone.0191154.
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