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Items: 1 to 20 of 100

1.

LAmbDA: Label Ambiguous Domain Adaptation Dataset Integration Reduces Batch Effects and Improves Subtype Detection.

Johnson TS, Wang T, Huang Z, Yu CY, Wu Y, Han Y, Zhang Y, Huang K, Zhang J.

Bioinformatics. 2019 Apr 30. pii: btz295. doi: 10.1093/bioinformatics/btz295. [Epub ahead of print]

PMID:
31038689
2.
3.

Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge.

Mukherjee S, Zhang Y, Fan J, Seelig G, Kannan S.

Bioinformatics. 2018 Jul 1;34(13):i124-i132. doi: 10.1093/bioinformatics/bty293.

4.

Cell-level somatic mutation detection from single-cell RNA-sequencing.

Vu TN, Nguyen HN, Calza S, Kalari KR, Wang L, Pawitan Y.

Bioinformatics. 2019 Apr 26. pii: btz288. doi: 10.1093/bioinformatics/btz288. [Epub ahead of print]

PMID:
31028395
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.

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.

7.

scMatch: a single-cell gene expression profile annotation tool using reference datasets.

Hou R, Denisenko E, Forrest ARR.

Bioinformatics. 2019 Apr 26. pii: btz292. doi: 10.1093/bioinformatics/btz292. [Epub ahead of print]

PMID:
31028376
8.

An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets.

Schmidt F, List M, Cukuroglu E, Köhler S, Göke J, Schulz MH.

Bioinformatics. 2018 Sep 1;34(17):i908-i916. doi: 10.1093/bioinformatics/bty553.

9.

ClusterMap: Compare multiple Single Cell RNA-Seq datasets across different experimental conditions.

Gao X, Hu D, Gogol M, Li H.

Bioinformatics. 2019 Jan 14. doi: 10.1093/bioinformatics/btz024. [Epub ahead of print]

PMID:
30649203
10.

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.

11.

Removal of batch effects using distribution-matching residual networks.

Shaham U, Stanton KP, Zhao J, Li H, Raddassi K, Montgomery R, Kluger Y.

Bioinformatics. 2017 Aug 15;33(16):2539-2546. doi: 10.1093/bioinformatics/btx196.

12.

DECENT: Differential Expression with Capture Efficiency adjustmeNT for single-cell RNA-seq data.

Ye C, Speed TP, Salim A.

Bioinformatics. 2019 Jun 14. pii: btz453. doi: 10.1093/bioinformatics/btz453. [Epub ahead of print]

PMID:
31197307
13.

Detecting hidden batch factors through data-adaptive adjustment for biological effects.

Yi H, Raman AT, Zhang H, Allen GI, Liu Z.

Bioinformatics. 2018 Apr 1;34(7):1141-1147. doi: 10.1093/bioinformatics/btx635.

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.

Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.

Stein-O'Brien GL, Clark BS, Sherman T, Zibetti C, Hu Q, Sealfon R, Liu S, Qian J, Colantuoni C, Blackshaw S, Goff LA, Fertig EJ.

Cell Syst. 2019 May 22;8(5):395-411.e8. doi: 10.1016/j.cels.2019.04.004.

16.

SinNLRR: a robust subspace clustering method for cell type detection by nonnegative and low rank representation.

Zheng R, Li M, Liang Z, Wu FX, Pan Y, Wang J.

Bioinformatics. 2019 Mar 1. pii: btz139. doi: 10.1093/bioinformatics/btz139. [Epub ahead of print]

PMID:
30821315
17.

SSCC: A Novel Computational Framework for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data.

Ren X, Zheng L, Zhang Z.

Genomics Proteomics Bioinformatics. 2019 Apr;17(2):201-210. doi: 10.1016/j.gpb.2018.10.003. Epub 2019 Jun 13.

18.

Single-cell RNA-seq Interpretations using Evolutionary Multiobjective Ensemble Pruning.

Li X, Zhang S, Wong KC.

Bioinformatics. 2018 Dec 28. doi: 10.1093/bioinformatics/bty1056. [Epub ahead of print]

PMID:
30596898
19.

PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes.

Papadopoulos N, Parra RG, Söding J.

Bioinformatics. 2019 Feb 1. doi: 10.1093/bioinformatics/btz078. [Epub ahead of print]

PMID:
30715210
20.

Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.

Hocking TD, Goerner-Potvin P, Morin A, Shao X, Pastinen T, Bourque G.

Bioinformatics. 2017 Feb 15;33(4):491-499. doi: 10.1093/bioinformatics/btw672.

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