Benchmarking of cell type deconvolution pipelines for transcriptomics data
Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.
Enregistré dans:
Auteurs principaux: | Francisco Avila Cobos, José Alquicira-Hernandez, Joseph E. Powell, Pieter Mestdagh, Katleen De Preter |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2020
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/bce8d3da8b7e49c7b03795b732b6ebd6 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Author Correction: Benchmarking of cell type deconvolution pipelines for transcriptomics data
par: Francisco Avila Cobos, et autres
Publié: (2020) -
Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data
par: Georgette Tanner, et autres
Publié: (2021) -
Benchmarking of RNA-sequencing analysis workflows using whole-transcriptome RT-qPCR expression data
par: Celine Everaert, et autres
Publié: (2017) -
Molecular characterization and cell type composition deconvolution of fibrosis in NAFLD
par: Lorena Pantano, et autres
Publié: (2021) -
Deconvolution of Bulk Gene Expression Profiles with Single-Cell Transcriptomics to Develop a Cell Type Composition-Based Prognostic Model for Acute Myeloid Leukemia
par: Chengguqiu Dai, et autres
Publié: (2021)