ArticleNature protocols2019
Guidelines for using sigQC for systematic evaluation of gene signatures.
Article in Nature protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Development of a Clinical Assay to Guide Patient Therapy in HPV-Associated Head and Neck Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Sigscores: summary scores for molecular signatures in R.Bioinformatics advances · 2026Article
- Defining hypoxia in cancer: A landmark evaluation of hypoxia gene expression signatures.Cell genomics · 2025Article
- A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome.Human genetics · 2024Article
- MiRNA expression as outcome predictor in pediatric AML: systematic evaluation of a new model.NPJ genomic medicine · 2024Article
- Article
- Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study.PLoS computational biology · 2024Article
- Noncanonical HPV carcinogenesis drives radiosensitization of head and neck tumors.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- m5C regulator-mediated methylation modification phenotypes characterized by distinct tumor microenvironment immune heterogenicity in colorectal cancer.Scientific reports · 2023Article
- Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature.NPJ precision oncology · 2023Article
- Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer.Heliyon · 2022Article
- NF-κB over-activation portends improved outcomes in HPV-associated head and neck cancer.Oncotarget · 2022Article
- Machine Learning Using Gene-Sets to Infer miRNA Function.Advances in experimental medicine and biology · 2022Article
- RosettaSX: Reliable gene expression signature scoring of cancer models and patients.Neoplasia (New York, N.Y.) · 2021Article
- Replication catastrophe induced by cyclic hypoxia leads to increased APOBEC3B activity.Nucleic acids research · 2021Article
- Radiosensitivity index emerges as a potential biomarker for combined radiotherapy and immunotherapy.NPJ genomic medicine · 2021Article
- Protein structure-based gene expression signatures.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization.Experimental & molecular medicine · 2021Review
- Mitochondrial Inhibitor Atovaquone Increases Tumor Oxygenation and Inhibits Hypoxic Gene Expression in Patients with Non-Small Cell Lung Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021Article
- Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges.Cancers · 2021Review
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Authors and funding
9 authors.
Funding
Abstract
With 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.
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