Interpretable factor models of single-cell RNA-seq via variational autoencoders is a research paper published in Bioinformatics (2020). On theSindex it has a DataRank of 0. It has been cited 221 times.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Institutes of Health
Grant: U19MH114830
National Institutes of Health
Grant: CZF2019-002454
NHGRI NIH HHS
Grant: T32 HG000047
National Institutes of Health
Grant: 5K05MH002019-05
NEURAL MECHANISM OF ATTENTIONAL CONTROL AND SELECTION
National Institutes of Health
Grant: 5U19MH114830-05
A comprehensive whole-brain atlas of cell types in the mouse
FWCI
9.71
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Analysis of single-cell RNA sequencing data based on autoencoders
Additional file 1 of Analysis of single-cell RNA sequencing data based on autoencoders
Additional file 1 of Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Additional file 1 of Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Additional file 3 of Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Additional file 3 of Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Additional file 1 of Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Additional file 2 of Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Additional file 1 of Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Additional file 2 of Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Additional file 1 of siVAE: interpretable deep generative models for single-cell transcriptomes
Additional file 1 of siVAE: interpretable deep generative models for single-cell transcriptomes
Additional file 2 of siVAE: interpretable deep generative models for single-cell transcriptomes
Additional file 2 of siVAE: interpretable deep generative models for single-cell transcriptomes
Additional file 1 of The effect of data transformation on low-dimensional integration of single-cell RNA-seq
Additional file 1 of The effect of data transformation on low-dimensional integration of single-cell RNA-seq
Additional file 1 of PAUSE: principled feature attribution for unsupervised gene expression analysis
Additional file 1 of PAUSE: principled feature attribution for unsupervised gene expression analysis
Additional file 4 of PAUSE: principled feature attribution for unsupervised gene expression analysis
Additional file 4 of PAUSE: principled feature attribution for unsupervised gene expression analysis