Camera: a competitive gene set test accounting for inter-gene correlation

By Diabetes-Ling Wu, Gordon K. Smyth. Nucleic Acids Research, 2012

802 citations recorded by the source corpus.

Competitive gene set tests are commonly used in molecular pathway analysis to test for enrichment of a particular gene annotation category amongst the differential expression results from a microarray experiment. Existing gene set tests that rely on gene permutation are shown here to be extremely sensitive to inter-gene correlation. Several data sets are analyzed to show that inter-gene correlation is non-ignorable even for experiments on homogeneous cell populations using genetically identical model organisms. A new gene set test procedure (CAMERA) is proposed based on the idea of estimating the inter-gene correlation from the data, and using it to adjust the gene set test statistic. An efficient procedure is developed for estimating the inter-gene correlation and characterizing its precision. CAMERA is shown to control the type I error rate correctly regardless of inter-gene correlations, yet retains excellent power for detecting genuine differential expression. Analysis of breast cancer data shows that CAMERA recovers known relationships between tumor subtypes in very convincing terms. CAMERA can be used to analyze specified sets or as a pathway analysis tool using a database of molecular signatures.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/045812b3b7796ceac18855d580a3fec7a5a80f66

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Citations in the corpus

limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “More sophisticated competitive tests that take into account dependence between the genes in the linear modelling framework are implemented in the camera function (56).”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, positive): “Gene set testing methods such as camera and fry can then be applied to summarize the results at gene level [ 60 , 61 ].”
T Cell Factor 1-Expressing Memory-like CD8(+) T Cells Sustain the Immune Response to Chronic Viral Infections. (2016, methodology): “Gene set enrichment was tested using camera (Wu and Smyth, 2012) against the ranked lists of moderated t-statistics from the comparison of Chronic Tcf1+ versus Chronic Tcf1- cells.”
MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (2015, methodology): “The approach is similar to that used by CAMERA [16] for bulk experiments in its accounting for inter-gene correlation that is known to inflate the false significance (type-I error) in permutation-based GSEA protocols [16], although it differs in that it uses the sampling variance of each model…”
Pathway and Network Analysis of Cancer Genomes (2015, positive): “New methods are developed to address these issues; for example CAMERA 34 corrects gene set enrichment statistics for inter-gene correlations.”
Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data (2021, neutral): “The pre-ranked version of CAMERA 54 (function name: cameraPR) in the limma R package (version 3.42.2) was used to evaluate the enriched pathways. cameraPR needs a pre-ranked gene list according to a user-defined statistic score.”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, methodology): “On the other hand, the voom expression values and weights are suitable for input into the ROAST and CAMERA procedures without any further processing.”