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• W2960798631 abstract "Abstract The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the trasncriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a computational pipeline using Jupyter notebooks. We also provide a user-friendly, cloud version of the notebook for researchers with very limited programming skills. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation datatset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. These notebooks can be used as tutorials for training purposes and will guide researchers to explore their scRNA-seq data." @default.
• W2960798631 created "2019-07-23" @default.
• W2960798631 creator A5012983566 @default.
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• W2960798631 date "2019-07-14" @default.
• W2960798631 modified "2023-09-23" @default.
• W2960798631 title "DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics" @default.
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• W2960798631 doi "https://doi.org/10.1101/700989" @default.
• W2960798631 hasPublicationYear "2019" @default.
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