Guidelines for using sigQC for systematic evaluation of gene signatures
West, Catharine ML
AffiliationComputational Biology and Integrative Genomics Lab, MRC/CRUK Oxford Institute and Department of Oncology, University of Oxford, Oxford, UK
MetadataShow full item record
AbstractWith the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.
CitationDhawan A, Barberis A, Cheng WC, Domingo E, West C, Maughan T, et al. Guidelines for using sigQC for systematic evaluation of gene signatures. Nat Protoc. 2019 May;14(5):1377-400.
- NOJAH: NOt Just Another Heatmap for genome-wide cluster analysis.
- Authors: Rupji M, Dwivedi B, Kowalski J
- Issue date: 2019
- The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis.
- Authors: Mohorianu I, Stocks MB, Applegate CS, Folkes L, Moulton V
- Issue date: 2017
- Next generation sequencing-based expression profiling identifies signatures from benign stromal proliferations that define stromal components of breast cancer.
- Authors: Guo X, Zhu SX, Brunner AL, van de Rijn M, West RB
- Issue date: 2013 Dec 17
- BubbleGUM: automatic extraction of phenotype molecular signatures and comprehensive visualization of multiple Gene Set Enrichment Analyses.
- Authors: Spinelli L, Carpentier S, Montañana Sanchis F, Dalod M, Vu Manh TP
- Issue date: 2015 Oct 19
- Quantitative comparison of microarray experiments with published leukemia related gene expression signatures.
- Authors: Klein HU, Ruckert C, Kohlmann A, Bullinger L, Thiede C, Haferlach T, Dugas M
- Issue date: 2009 Dec 15