These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
22. A statistical simulator scDesign for rational scRNA-seq experimental design. Li WV; Li JJ Bioinformatics; 2019 Jul; 35(14):i41-i50. PubMed ID: 31510652 [TBL] [Abstract][Full Text] [Related]
23. deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues. Pei G; Yan F; Simon LM; Dai Y; Jia P; Zhao Z Genomics Proteomics Bioinformatics; 2023 Apr; 21(2):370-384. PubMed ID: 35470070 [TBL] [Abstract][Full Text] [Related]
24. 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; 33(19):3123-3125. PubMed ID: 28541377 [TBL] [Abstract][Full Text] [Related]
25. Differential expression of single-cell RNA-seq data using Tweedie models. Mallick H; Chatterjee S; Chowdhury S; Chatterjee S; Rahnavard A; Hicks SC Stat Med; 2022 Aug; 41(18):3492-3510. PubMed ID: 35656596 [TBL] [Abstract][Full Text] [Related]
27. Quality control of single-cell RNA-seq by SinQC. Jiang P; Thomson JA; Stewart R Bioinformatics; 2016 Aug; 32(16):2514-6. PubMed ID: 27153613 [TBL] [Abstract][Full Text] [Related]
28. 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; 17 Suppl 7(Suppl 7):508. PubMed ID: 27556924 [TBL] [Abstract][Full Text] [Related]
29. scRNABatchQC: multi-samples quality control for single cell RNA-seq data. Liu Q; Sheng Q; Ping J; Ramirez MA; Lau KS; Coffey RJ; Shyr Y Bioinformatics; 2019 Dec; 35(24):5306-5308. PubMed ID: 31373345 [TBL] [Abstract][Full Text] [Related]
31. Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data. Cheng J; Smyth GK; Chen Y F1000Res; 2023; 12():684. PubMed ID: 37994351 [TBL] [Abstract][Full Text] [Related]
32. CaSee: A lightning transfer-learning model directly used to discriminate cancer/normal cells from scRNA-seq. Sh Y; Zhang X; Yang Z; Dong J; Wang Y; Zhou Y; Li X; Guo C; Hu Z Oncogene; 2022 Oct; 41(44):4866-4876. PubMed ID: 36192479 [TBL] [Abstract][Full Text] [Related]
33. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data. Li Z; Wang Y; Ganan-Gomez I; Colla S; Do KA Bioinformatics; 2022 Oct; 38(21):4885-4892. PubMed ID: 36083008 [TBL] [Abstract][Full Text] [Related]
34. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. McCarthy DJ; Campbell KR; Lun AT; Wills QF Bioinformatics; 2017 Apr; 33(8):1179-1186. PubMed ID: 28088763 [TBL] [Abstract][Full Text] [Related]
35. SC1: A Tool for Interactive Web-Based Single-Cell RNA-Seq Data Analysis. Moussa M; Măndoiu II J Comput Biol; 2021 Aug; 28(8):820-841. PubMed ID: 34115950 [TBL] [Abstract][Full Text] [Related]