ArticleBioinformatics (Oxford, England)2020
SINC: a scale-invariant deep-neural-network classifier for bulk and single-cell RNA-seq data.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Robust subspace structure discovery for cell type identification in scRNA-seq data.BMC bioinformatics · 2025Article
- IC2Bert: masked gene expression pretraining and supervised fine tuning for robust immune checkpoint blockade (ICB) response prediction.Scientific reports · 2025Article
- scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.BMC genomics · 2024Article
- TabDEG: Classifying differentially expressed genes from RNA-seq data based on feature extraction and deep learning framework.PloS one · 2024Article
- scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.BMC genomics · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
motivationScaling by sequencing depth is usually the first step of analysis of bulk or single-cell RNA-seq data, but estimating sequencing depth accurately can be difficult, especially for single-cell data, risking the validity of downstream analysis. It is thus of interest to eliminate the use of sequencing depth and analyze the original count data directly.
resultsWe call an analysis method 'scale-invariant' (SI) if it gives the same result under different estimates of sequencing depth and hence can use the original count data without scaling. For the problem of classifying samples into pre-specified classes, such as normal versus cancerous, we develop a deep-neural-network based SI classifier named scale-invariant deep neural-network classifier (SINC). On nine bulk and single-cell datasets, the classification accuracy of SINC is better than or competitive to the best of eight other classifiers. SINC is easier to use and more reliable on data where proper sequencing depth is hard to determine. AVAILABILITY AND IMPLEMENTATION: This source code of SINC is available at https://www.nd.edu/∼jli9/SINC.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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