185 related articles for article (PubMed ID: 35514182)
1. A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data.
Zhu B; Li H; Zhang L; Chandra SS; Zhao H
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35514182
[TBL] [Abstract][Full Text] [Related]
2. A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies.
Das S; Rai A; Merchant ML; Cave MC; Rai SN
Genes (Basel); 2021 Dec; 12(12):. PubMed ID: 34946896
[TBL] [Abstract][Full Text] [Related]
3. A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data.
Li H; Zhu B; Xu Z; Adams T; Kaminski N; Zhao H
BMC Bioinformatics; 2021 Oct; 22(1):524. PubMed ID: 34702190
[TBL] [Abstract][Full Text] [Related]
4. Detection of differentially expressed genes in discrete single-cell RNA sequencing data using a hurdle model with correlated random effects.
Sekula M; Gaskins J; Datta S
Biometrics; 2019 Dec; 75(4):1051-1062. PubMed ID: 31009065
[TBL] [Abstract][Full Text] [Related]
5. Cell Heterogeneity Analysis in Single-Cell RNA-seq Data Using Mixture Exponential Graph and Markov Random Field Model.
Wang Y; Tian X; Ai D
Biomed Res Int; 2021; 2021():9919080. PubMed ID: 34095314
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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; 14(4):e1006053. PubMed ID: 29630593
[TBL] [Abstract][Full Text] [Related]
8. Inverse weighting method with jackknife variance estimator for differential expression analysis of single-cell RNA sequencing data.
Zhou L; Pan Q
Comput Biol Chem; 2022 Oct; 100():107733. PubMed ID: 35926443
[TBL] [Abstract][Full Text] [Related]
9. scCODE: an R package for data-specific differentially expressed gene detection on single-cell RNA-sequencing data.
Zou J; Deng F; Wang M; Zhang Z; Liu Z; Zhang X; Hua R; Chen K; Zou X; Hao J
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35598331
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data.
Crowell HL; Soneson C; Germain PL; Calini D; Collin L; Raposo C; Malhotra D; Robinson MD
Nat Commun; 2020 Nov; 11(1):6077. PubMed ID: 33257685
[TBL] [Abstract][Full Text] [Related]
12. scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets.
Liu H; Li H; Sharma A; Huang W; Pan D; Gu Y; Lin L; Sun X; Liu H
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37183449
[TBL] [Abstract][Full Text] [Related]
13. Data Analysis in Single-Cell Transcriptome Sequencing.
Gao S
Methods Mol Biol; 2018; 1754():311-326. PubMed ID: 29536451
[TBL] [Abstract][Full Text] [Related]
14. MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features.
Zhu M; Li C; Lv K; Guo H; Hou R; Tian G; Yang J
Comput Biol Med; 2023 Jun; 159():106873. PubMed ID: 37105115
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data.
Junttila S; Smolander J; Elo LL
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35880426
[TBL] [Abstract][Full Text] [Related]
17. Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies.
Guo X; Ning J; Chen Y; Liu G; Zhao L; Fan Y; Sun S
Brief Funct Genomics; 2024 Mar; 23(2):95-109. PubMed ID: 37022699
[TBL] [Abstract][Full Text] [Related]
18. 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. PubMed ID: 30510133
[TBL] [Abstract][Full Text] [Related]
19. Random forest based similarity learning for single cell RNA sequencing data.
Pouyan MB; Kostka D
Bioinformatics; 2018 Jul; 34(13):i79-i88. PubMed ID: 29950006
[TBL] [Abstract][Full Text] [Related]
20. LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis.
Lin L; Zhang Y; Qian W; Liu Y; Zhang Y; Lin F; Liu C; Lu G; Sun D; Guo X; Song Y; Song J; Yang C; Li J
Proc Natl Acad Sci U S A; 2022 Feb; 119(5):. PubMed ID: 35086932
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]