755 related articles for article (PubMed ID: 31510660)
1. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology.
Sturm G; Finotello F; Petitprez F; Zhang JD; Baumbach J; Fridman WH; List M; Aneichyk T
Bioinformatics; 2019 Jul; 35(14):i436-i445. PubMed ID: 31510660
[TBL] [Abstract][Full Text] [Related]
2. NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution.
Tang D; Park S; Zhao H
Bioinformatics; 2020 Mar; 36(5):1344-1350. PubMed ID: 31593244
[TBL] [Abstract][Full Text] [Related]
3. A2Sign: Agnostic Algorithms for Signatures-a universal method for identifying molecular signatures from transcriptomic datasets prior to cell-type deconvolution.
Boldina G; Fogel P; Rocher C; Bettembourg C; Luta G; Augé F
Bioinformatics; 2022 Jan; 38(4):1015-1021. PubMed ID: 34788798
[TBL] [Abstract][Full Text] [Related]
4. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics.
Sang-Aram C; Browaeys R; Seurinck R; Saeys Y
Elife; 2024 May; 12():. PubMed ID: 38787371
[TBL] [Abstract][Full Text] [Related]
5. Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data.
Sturm G; Finotello F; List M
Methods Mol Biol; 2020; 2120():223-232. PubMed ID: 32124323
[TBL] [Abstract][Full Text] [Related]
6. RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.
Monaco G; Lee B; Xu W; Mustafah S; Hwang YY; Carré C; Burdin N; Visan L; Ceccarelli M; Poidinger M; Zippelius A; Pedro de Magalhães J; Larbi A
Cell Rep; 2019 Feb; 26(6):1627-1640.e7. PubMed ID: 30726743
[TBL] [Abstract][Full Text] [Related]
7. DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification.
Decamps C; Arnaud A; Petitprez F; Ayadi M; Baurès A; Armenoult L; ; Escalera S; Guyon I; Nicolle R; Tomasini R; de Reyniès A; Cros J; Blum Y; Richard M
BMC Bioinformatics; 2021 Oct; 22(1):473. PubMed ID: 34600479
[TBL] [Abstract][Full Text] [Related]
8. CATD: a reproducible pipeline for selecting cell-type deconvolution methods across tissues.
Vathrakokoili Pournara A; Miao Z; Beker OY; Nolte N; Brazma A; Papatheodorou I
Bioinform Adv; 2024; 4(1):vbae048. PubMed ID: 38638280
[TBL] [Abstract][Full Text] [Related]
9. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.
Huuki-Myers LA; Montgomery KD; Kwon SH; Cinquemani S; Eagles NJ; Gonzalez-Padilla D; Maden SK; Kleinman JE; Hyde TM; Hicks SC; Maynard KR; Collado-Torres L
bioRxiv; 2024 Apr; ():. PubMed ID: 38405805
[TBL] [Abstract][Full Text] [Related]
10. IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures.
Zeng D; Ye Z; Shen R; Yu G; Wu J; Xiong Y; Zhou R; Qiu W; Huang N; Sun L; Li X; Bin J; Liao Y; Shi M; Liao W
Front Immunol; 2021; 12():687975. PubMed ID: 34276676
[TBL] [Abstract][Full Text] [Related]
11. EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data.
Racle J; Gfeller D
Methods Mol Biol; 2020; 2120():233-248. PubMed ID: 32124324
[TBL] [Abstract][Full Text] [Related]
12. dtangle: accurate and robust cell type deconvolution.
Hunt GJ; Freytag S; Bahlo M; Gagnon-Bartsch JA
Bioinformatics; 2019 Jun; 35(12):2093-2099. PubMed ID: 30407492
[TBL] [Abstract][Full Text] [Related]
13. Tissue-specific deconvolution of immune cell composition by integrating bulk and single-cell transcriptomes.
Chen Z; Ji C; Shen Q; Liu W; Qin FX; Wu A
Bioinformatics; 2020 Feb; 36(3):819-827. PubMed ID: 31504185
[TBL] [Abstract][Full Text] [Related]
14. In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy.
Sturm G; Finotello F; List M
Methods Mol Biol; 2020; 2120():213-222. PubMed ID: 32124322
[TBL] [Abstract][Full Text] [Related]
15. Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle.
Tekath T; Dugas M
Bioinformatics; 2021 Nov; 37(21):3781-3787. PubMed ID: 34469510
[TBL] [Abstract][Full Text] [Related]
16. CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data.
Kang K; Meng Q; Shats I; Umbach DM; Li M; Li Y; Li X; Li L
PLoS Comput Biol; 2019 Dec; 15(12):e1007510. PubMed ID: 31790389
[TBL] [Abstract][Full Text] [Related]
17. Detecting retinal neural and stromal cell classes and ganglion cell subtypes based on transcriptome data with deep transfer learning.
Madadi Y; Sun J; Chen H; Williams R; Yousefi S
Bioinformatics; 2022 Sep; 38(18):4321-4329. PubMed ID: 35876552
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. SCORE: Smart Consensus Of RNA Expression-a consensus tool for detecting differentially expressed genes in bacteria.
Wolf SA; Epping L; Andreotti S; Reinert K; Semmler T
Bioinformatics; 2021 Apr; 37(3):426-428. PubMed ID: 32717040
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
