246 related articles for article (PubMed ID: 35325552)
1. Correlation Imputation for Single-Cell RNA-seq.
Gan L; Vinci G; Allen GI
J Comput Biol; 2022 May; 29(5):465-482. PubMed ID: 35325552
[No Abstract] [Full Text] [Related]
2. Correlation Imputation in Single cell RNA-seq using Auxiliary Information and Ensemble Learning.
Gan L; Vinci G; Allen GI
ACM BCB; 2020 Sep; 2020():. PubMed ID: 34278382
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. CL-Impute: A contrastive learning-based imputation for dropout single-cell RNA-seq data.
Shi Y; Wan J; Zhang X; Yin Y
Comput Biol Med; 2023 Sep; 164():107263. PubMed ID: 37531858
[TBL] [Abstract][Full Text] [Related]
5. AutoImpute: Autoencoder based imputation of single-cell RNA-seq data.
Talwar D; Mongia A; Sengupta D; Majumdar A
Sci Rep; 2018 Nov; 8(1):16329. PubMed ID: 30397240
[TBL] [Abstract][Full Text] [Related]
6. SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares.
Gong L; Cui X; Liu Y; Lin C; Gao Z
Comput Biol Med; 2024 Mar; 171():108225. PubMed ID: 38442556
[TBL] [Abstract][Full Text] [Related]
7. Improvements Achieved by Multiple Imputation for Single-Cell RNA-Seq Data in Clustering Analysis and Differential Expression Analysis.
Zhu M; Lai Y
J Comput Biol; 2022 Jul; 29(7):634-649. PubMed ID: 35575729
[TBL] [Abstract][Full Text] [Related]
8. Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.
Xu X; Yu X; Hu G; Wang K; Zhang J; Li X
Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35821114
[TBL] [Abstract][Full Text] [Related]
9. Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data.
Chen S; Yan X; Zheng R; Li M
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36567258
[TBL] [Abstract][Full Text] [Related]
10. CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts.
Azim R; Wang S; Dipu SA
Comput Biol Med; 2022 Jul; 146():105658. PubMed ID: 35751187
[TBL] [Abstract][Full Text] [Related]
11. scGCL: an imputation method for scRNA-seq data based on graph contrastive learning.
Xiong Z; Luo J; Shi W; Liu Y; Xu Z; Wang B
Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36825817
[TBL] [Abstract][Full Text] [Related]
12. Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.
Gan Y; Huang X; Zou G; Zhou S; Guan J
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35172334
[TBL] [Abstract][Full Text] [Related]
13. A deep matrix factorization based approach for single-cell RNA-seq data clustering.
Liang Z; Zheng R; Chen S; Yan X; Li M
Methods; 2022 Sep; 205():114-122. PubMed ID: 35777719
[TBL] [Abstract][Full Text] [Related]
14. ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.
Malec M; Kurban H; Dalkilic M
BMC Bioinformatics; 2022 Jul; 23(1):291. PubMed ID: 35869420
[TBL] [Abstract][Full Text] [Related]
15. netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.
Elyanow R; Dumitrascu B; Engelhardt BE; Raphael BJ
Genome Res; 2020 Feb; 30(2):195-204. PubMed ID: 31992614
[TBL] [Abstract][Full Text] [Related]
16. A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data.
Qi Y; Guo Y; Jiao H; Shang X
BMC Bioinformatics; 2020 Jun; 21(1):240. PubMed ID: 32527285
[TBL] [Abstract][Full Text] [Related]
17. scCAN: Clustering With Adaptive Neighbor-Based Imputation Method for Single-Cell RNA-Seq Data.
Dong S; Liu Y; Gong Y; Dong X; Zeng X
IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(1):95-105. PubMed ID: 38285569
[TBL] [Abstract][Full Text] [Related]
18. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
[TBL] [Abstract][Full Text] [Related]
19. Machine learning and statistical methods for clustering single-cell RNA-sequencing data.
Petegrosso R; Li Z; Kuang R
Brief Bioinform; 2020 Jul; 21(4):1209-1223. PubMed ID: 31243426
[TBL] [Abstract][Full Text] [Related]
20. Consensus clustering of single-cell RNA-seq data by enhancing network affinity.
Cui Y; Zhang S; Liang Y; Wang X; Ferraro TN; Chen Y
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34160582
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]