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Sökning: WFRF:(Jonasson Emma 1987) > (2021) > DIscBIO: A User-Fri...

DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

Ghannoum, S. (författare)
Waldir, N. (författare)
Fantini, D. (författare)
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Ragan-Kelley, B. (författare)
Parizadeh, A. (författare)
Jonasson, Emma, 1987 (författare)
Gothenburg University,Göteborgs universitet,Sahlgrenska Centrum för Cancerforskning (SCCR),Institutionen för biomedicin, avdelningen för laboratoriemedicin,Sahlgrenska Center for Cancer Research (SCCR),Department of Laboratory Medicine
Ståhlberg, Anders, 1975 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för biomedicin, avdelningen för laboratoriemedicin,Wallenberg Centre for Molecular and Translational Medicine,Sahlgrenska Centrum för Cancerforskning (SCCR),Department of Laboratory Medicine,Sahlgrenska Center for Cancer Research (SCCR)
Farhan, H. (författare)
Kohn-Luque, A. (författare)
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 (creator_code:org_t)
2021-01-30
2021
Engelska.
Ingår i: International Journal of Molecular Sciences. - : MDPI AG. - 1422-0067. ; 22:3
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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 transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a 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 user-friendly computational pipeline using Jupyter notebooks. 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 dataset 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. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Medicinsk genetik (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Medical Genetics (hsv//eng)

Nyckelord

single-cell sequencing
normalization
gene filtering
ERCC spike-ins
biomarkers
DEGs
decision trees
network analysis
Jupyter notebook
binder
Biochemistry & Molecular Biology
Chemistry

Publikations- och innehållstyp

ref (ämneskategori)
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