125 related articles for article (PubMed ID: 35004214)
1. Statistical methods for analysis of single-cell RNA-sequencing data.
Das S; Rai SN
MethodsX; 2021; 8():101580. PubMed ID: 35004214
[TBL] [Abstract][Full Text] [Related]
2. SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data.
Das S; Rai SN
Genomics; 2021 May; 113(3):1308-1324. PubMed ID: 33662531
[TBL] [Abstract][Full Text] [Related]
3. ZERO-INFLATED QUANTILE RANK-SCORE BASED TEST (ZIQRANK) WITH APPLICATION TO SCRNA-SEQ DIFFERENTIAL GENE EXPRESSION ANALYSIS.
Ling W; Zhang W; Cheng B; Wei Y
Ann Appl Stat; 2021 Dec; 15(4):1673-1696. PubMed ID: 35116085
[TBL] [Abstract][Full Text] [Related]
4. UMI-count modeling and differential expression analysis for single-cell RNA sequencing.
Chen W; Li Y; Easton J; Finkelstein D; Wu G; Chen X
Genome Biol; 2018 May; 19(1):70. PubMed ID: 29855333
[TBL] [Abstract][Full Text] [Related]
5. Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.
Yang Z; Ho YY
Biometrics; 2022 Jun; 78(2):766-776. PubMed ID: 33720414
[TBL] [Abstract][Full Text] [Related]
6. Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.
Lause J; Berens P; Kobak D
Genome Biol; 2021 Sep; 22(1):258. PubMed ID: 34488842
[TBL] [Abstract][Full Text] [Related]
7. Detecting differential alternative splicing events in scRNA-seq with or without Unique Molecular Identifiers.
Hu Y; Wang K; Li M
PLoS Comput Biol; 2020 Jun; 16(6):e1007925. PubMed ID: 32502143
[TBL] [Abstract][Full Text] [Related]
8. Gene length and detection bias in single cell RNA sequencing protocols.
Phipson B; Zappia L; Oshlack A
F1000Res; 2017; 6():595. PubMed ID: 28529717
[No Abstract] [Full Text] [Related]
9. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.
Van den Berge K; Perraudeau F; Soneson C; Love MI; Risso D; Vert JP; Robinson MD; Dudoit S; Clement L
Genome Biol; 2018 Feb; 19(1):24. PubMed ID: 29478411
[TBL] [Abstract][Full Text] [Related]
10. Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data.
Dadaneh SZ; de Figueiredo P; Sze SH; Zhou M; Qian X
BMC Genomics; 2020 Sep; 21(Suppl 9):585. PubMed ID: 32900358
[TBL] [Abstract][Full Text] [Related]
11. A machine learning framework for scRNA-seq UMI threshold optimization and accurate classification of cell types.
Bishara I; Chen J; Griffiths JI; Bild AH; Nath A
Front Genet; 2022; 13():982019. PubMed ID: 36506328
[TBL] [Abstract][Full Text] [Related]
12. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression.
Hafemeister C; Satija R
Genome Biol; 2019 Dec; 20(1):296. PubMed ID: 31870423
[TBL] [Abstract][Full Text] [Related]
13. iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.
Liu Y; Zhao J; Adams TS; Wang N; Schupp JC; Wu W; McDonough JE; Chupp GL; Kaminski N; Wang Z; Yan X
BMC Bioinformatics; 2023 Aug; 24(1):318. PubMed ID: 37608264
[TBL] [Abstract][Full Text] [Related]
14. scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information.
Qi J; Sheng Q; Zhou Y; Hua J; Xiao S; Jin S
Cell Biosci; 2022 Sep; 12(1):142. PubMed ID: 36056412
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics.
Choi K; Chen Y; Skelly DA; Churchill GA
Genome Biol; 2020 Jul; 21(1):183. PubMed ID: 32718323
[TBL] [Abstract][Full Text] [Related]
17. Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level.
Sarkar H; Srivastava A; Patro R
Bioinformatics; 2019 Jul; 35(14):i136-i144. PubMed ID: 31510649
[TBL] [Abstract][Full Text] [Related]
18. Comparison and evaluation of statistical error models for scRNA-seq.
Choudhary S; Satija R
Genome Biol; 2022 Jan; 23(1):27. PubMed ID: 35042561
[TBL] [Abstract][Full Text] [Related]
19. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model.
Townes FW; Hicks SC; Aryee MJ; Irizarry RA
Genome Biol; 2019 Dec; 20(1):295. PubMed ID: 31870412
[TBL] [Abstract][Full Text] [Related]
20. GE-Impute: graph embedding-based imputation for single-cell RNA-seq data.
Wu X; Zhou Y
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35901457
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]