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Items: 1 to 20 of 99

1.

PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq.

Tyler SR, Rotti PG, Sun X, Yi Y, Xie W, Winter MC, Flamme-Wiese MJ, Tucker BA, Mullins RF, Norris AW, Engelhardt JF.

Cell Rep. 2019 Feb 12;26(7):1951-1964.e8. doi: 10.1016/j.celrep.2019.01.063.

2.

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.

3.

Understanding the Biology and Pathogenesis of the Kidney by Single-Cell Transcriptomic Analysis.

Ye Y, Song H, Zhang J, Shi S.

Kidney Dis (Basel). 2018 Nov;4(4):214-225. doi: 10.1159/000492470. Epub 2018 Sep 3. Review.

4.

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method.

Gan Y, Li N, Zou G, Xin Y, Guan J.

BMC Med Genomics. 2018 Dec 31;11(Suppl 6):117. doi: 10.1186/s12920-018-0433-z.

5.

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.

6.

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.

7.

Detection of high variability in gene expression from single-cell RNA-seq profiling.

Chen HI, Jin Y, Huang Y, Chen Y.

BMC Genomics. 2016 Aug 22;17 Suppl 7:508. doi: 10.1186/s12864-016-2897-6.

8.

Cell-specific network constructed by single-cell RNA sequencing data.

Dai H, Li L, Zeng T, Chen L.

Nucleic Acids Res. 2019 Mar 13. pii: gkz172. doi: 10.1093/nar/gkz172. [Epub ahead of print]

PMID:
30864667
9.

Random forest based similarity learning for single cell RNA sequencing data.

Pouyan MB, Kostka D.

Bioinformatics. 2018 Jul 1;34(13):i79-i88. doi: 10.1093/bioinformatics/bty260.

10.

Impact of similarity metrics on single-cell RNA-seq data clustering.

Kim T, Chen IR, Lin Y, Wang AY, Yang JYH, Yang P.

Brief Bioinform. 2018 Aug 22. doi: 10.1093/bib/bby076. [Epub ahead of print]

PMID:
30137247
11.

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
12.

Advantages of Single-Nucleus over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis.

Wu H, Kirita Y, Donnelly EL, Humphreys BD.

J Am Soc Nephrol. 2019 Jan;30(1):23-32. doi: 10.1681/ASN.2018090912. Epub 2018 Dec 3.

PMID:
30510133
13.

Impact of sequencing depth and read length on single cell RNA sequencing data of T cells.

Rizzetto S, Eltahla AA, Lin P, Bull R, Lloyd AR, Ho JWK, Venturi V, Luciani F.

Sci Rep. 2017 Oct 6;7(1):12781. doi: 10.1038/s41598-017-12989-x.

14.

ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.

Gardeux V, David FPA, Shajkofci A, Schwalie PC, Deplancke B.

Bioinformatics. 2017 Oct 1;33(19):3123-3125. doi: 10.1093/bioinformatics/btx337.

15.

Single-cell transcriptomics of East-Asian pancreatic islets cells.

Dorajoo R, Ali Y, Tay VSY, Kang J, Samydurai S, Liu J, Boehm BO.

Sci Rep. 2017 Jul 10;7(1):5024. doi: 10.1038/s41598-017-05266-4.

16.

scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells.

Cao Y, Zhu J, Jia P, Zhao Z.

Genes (Basel). 2017 Dec 5;8(12). pii: E368. doi: 10.3390/genes8120368.

17.

CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.

Müller S, Cho A, Liu SJ, Lim DA, Diaz A.

Bioinformatics. 2018 Sep 15;34(18):3217-3219. doi: 10.1093/bioinformatics/bty316.

PMID:
29897414
18.

Gene length and detection bias in single cell RNA sequencing protocols.

Phipson B, Zappia L, Oshlack A.

F1000Res. 2017 Apr 28;6:595. doi: 10.12688/f1000research.11290.1. eCollection 2017.

19.

scRNA-Seq reveals distinct stem cell populations that drive hair cell regeneration after loss of Fgf and Notch signaling.

Lush ME, Diaz DC, Koenecke N, Baek S, Boldt H, St Peter MK, Gaitan-Escudero T, Romero-Carvajal A, Busch-Nentwich EM, Perera AG, Hall KE, Peak A, Haug JS, Piotrowski T.

Elife. 2019 Jan 25;8. pii: e44431. doi: 10.7554/eLife.44431.

20.

Quality control of single-cell RNA-seq by SinQC.

Jiang P, Thomson JA, Stewart R.

Bioinformatics. 2016 Aug 15;32(16):2514-6. doi: 10.1093/bioinformatics/btw176. Epub 2016 Apr 10.

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