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Items: 1 to 20 of 145

1.

A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies.

Sun Z, Chen L, Xin H, Jiang Y, Huang Q, Cillo AR, Tabib T, Kolls JK, Bruno TC, Lafyatis R, Vignali DAA, Chen K, Ding Y, Hu M, Chen W.

Nat Commun. 2019 Apr 9;10(1):1649. doi: 10.1038/s41467-019-09639-3.

2.

DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

Sun Z, Wang T, Deng K, Wang XF, Lafyatis R, Ding Y, Hu M, Chen W.

Bioinformatics. 2018 Jan 1;34(1):139-146. doi: 10.1093/bioinformatics/btx490.

3.

Data Analysis in Single-Cell Transcriptome Sequencing.

Gao S.

Methods Mol Biol. 2018;1754:311-326. doi: 10.1007/978-1-4939-7717-8_18.

PMID:
29536451
4.

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.

Zhang H, Lee CAA, Li Z, Garbe JR, Eide CR, Petegrosso R, Kuang R, Tolar J.

PLoS Comput Biol. 2018 Apr 9;14(4):e1006053. doi: 10.1371/journal.pcbi.1006053. eCollection 2018 Apr.

5.

A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications.

Peng H, Zeng X, Zhou Y, Zhang D, Nussinov R, Cheng F.

PLoS Comput Biol. 2019 Feb 19;15(2):e1006772. doi: 10.1371/journal.pcbi.1006772. eCollection 2019 Feb.

6.

Identification of Cell Types from Single-Cell Transcriptomic Data.

Shekhar K, Menon V.

Methods Mol Biol. 2019;1935:45-77. doi: 10.1007/978-1-4939-9057-3_4.

PMID:
30758819
7.

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Butler A, Hoffman P, Smibert P, Papalexi E, Satija R.

Nat Biotechnol. 2018 Jun;36(5):411-420. doi: 10.1038/nbt.4096. Epub 2018 Apr 2.

PMID:
29608179
8.

The promise of single-cell RNA sequencing for kidney disease investigation.

Wu H, Humphreys BD.

Kidney Int. 2017 Dec;92(6):1334-1342. doi: 10.1016/j.kint.2017.06.033. Epub 2017 Oct 12. Review.

9.

Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.

Tian L, Dong X, Freytag S, LĂȘ Cao KA, Su S, JalalAbadi A, Amann-Zalcenstein D, Weber TS, Seidi A, Jabbari JS, Naik SH, Ritchie ME.

Nat Methods. 2019 Jun;16(6):479-487. doi: 10.1038/s41592-019-0425-8. Epub 2019 May 27.

PMID:
31133762
10.

Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data.

Huang Y, Sanguinetti G.

Methods Mol Biol. 2019;1935:175-185. doi: 10.1007/978-1-4939-9057-3_12.

PMID:
30758827
11.

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database.

Zappia L, Phipson B, Oshlack A.

PLoS Comput Biol. 2018 Jun 25;14(6):e1006245. doi: 10.1371/journal.pcbi.1006245. eCollection 2018 Jun.

12.

Bias, robustness and scalability in single-cell differential expression analysis.

Soneson C, Robinson MD.

Nat Methods. 2018 Apr;15(4):255-261. doi: 10.1038/nmeth.4612. Epub 2018 Feb 26.

PMID:
29481549
13.

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

Haghverdi L, Lun ATL, Morgan MD, Marioni JC.

Nat Biotechnol. 2018 Jun;36(5):421-427. doi: 10.1038/nbt.4091. Epub 2018 Apr 2.

14.
15.

SC3: consensus clustering of single-cell RNA-seq data.

Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, Hemberg M.

Nat Methods. 2017 May;14(5):483-486. doi: 10.1038/nmeth.4236. Epub 2017 Mar 27.

16.

SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis.

Guo M, Wang H, Potter SS, Whitsett JA, Xu Y.

PLoS Comput Biol. 2015 Nov 24;11(11):e1004575. doi: 10.1371/journal.pcbi.1004575. eCollection 2015 Nov.

17.

CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.

Lin P, Troup M, Ho JW.

Genome Biol. 2017 Mar 28;18(1):59. doi: 10.1186/s13059-017-1188-0.

18.

JingleBells: A Repository of Immune-Related Single-Cell RNA-Sequencing Datasets.

Ner-Gaon H, Melchior A, Golan N, Ben-Haim Y, Shay T.

J Immunol. 2017 May 1;198(9):3375-3379. doi: 10.4049/jimmunol.1700272.

19.

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data.

Chen S, Hua K, Cui H, Jiang R.

BMC Bioinformatics. 2019 May 1;20(Suppl 7):0. doi: 10.1186/s12859-019-2742-4.

20.

Computational approaches for interpreting scRNA-seq data.

Rostom R, Svensson V, Teichmann SA, Kar G.

FEBS Lett. 2017 Aug;591(15):2213-2225. doi: 10.1002/1873-3468.12684. Epub 2017 Jun 12. Review.

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