ArticleChinese journal of cancer2015
Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients.
Article in Chinese journal of cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Approaches for sRNA Analysis of Human RNA-Seq Data: Comparison, Benchmarking.International journal of molecular sciences · 2023Article
- Impact of Variable RNA-Sequencing Depth on Gene Expression Signatures and Target Compound Robustness: Case Study Examining Brain Tumor (Glioma) Disease Progression.JCO precision oncology · 2018Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundData from RNA-seq experiments provide a wealth of information about the transcriptome of an organism. However, the analysis of such data is very demanding. In this study, we aimed to establish robust analysis procedures that can be used in clinical practice.
methodsWe studied RNA-seq data from triple-negative breast cancer patients. Specifically, we investigated the subsampling of RNA-seq data.
resultsThe main results of our investigations are as follows: (1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices; (2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads; and (3) for an abrogated feature selection, higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values.
conclusionsOur results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.
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