Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding is a research paper published in Science (2018). On theSindex it has a DataRank of 1.1. It has been cited 1,513 times.
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Base Score Contribution
1.1
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Science Foundation
Grant: CCF-1317653
National Institutes of Health
Grant: TL1 TR002318
National Institute of General Medical Sciences
Grant: R01CA207029
National Institute of Neurological Disorders and Stroke
Grant: R01NS064404
National Institute of Neurological Disorders and Stroke
Grant: R21NS086500
FWCI
53.36
Citation Percentile
1.0%
Influential Citations
49
Citation Trend
Fields of Study
MeSH Terms
Keywords
Additional file 1 of Decode-seq: a practical approach to improve differential gene expression analysis
Additional file 1 of Decode-seq: a practical approach to improve differential gene expression analysis
Additional file 3 of Decode-seq: a practical approach to improve differential gene expression analysis
Additional file 3 of Decode-seq: a practical approach to improve differential gene expression analysis
Additional file 5 of Decode-seq: a practical approach to improve differential gene expression analysis
Additional file 5 of Decode-seq: a practical approach to improve differential gene expression analysis
Additional file 1 of DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning
Additional file 1 of DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning
Additional file 2 of DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning
Additional file 2 of DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning
Additional file 12 of Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs
Additional file 12 of Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs
Additional file 1 of Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs
Additional file 1 of Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs
Additional file 3 of Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs
Additional file 3 of Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs
Additional file 1 of Selecting single cell clustering parameter values using subsampling-based robustness metrics
Additional file 1 of Selecting single cell clustering parameter values using subsampling-based robustness metrics
Additional file 2 of Selecting single cell clustering parameter values using subsampling-based robustness metrics
Additional file 2 of Selecting single cell clustering parameter values using subsampling-based robustness metrics