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- Title
scAmpi—A versatile pipeline for single-cell RNA-seq analysis from basics to clinics.
- Authors
Bertolini, Anne; Prummer, Michael; Tuncel, Mustafa Anil; Menzel, Ulrike; Rosano-González, María Lourdes; Kuipers, Jack; Stekhoven, Daniel Johannes; Beerenwinkel, Niko; Singer, Franziska
- Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study. Author summary: Single-cell RNA sequencing (scRNA-seq) measures the expression levels across the genes expressed in each single cell. Thus, it is well suited to inform on the cell type composition and the function of cells in different tissues and diseases. However, it is challenging to correctly process and interpret the large amounts of data generated with scRNA-seq. To this end, we have developed an analysis workflow named scAmpi (Single Cell Analysis mRNA pipeline) that starts on the raw sequencing data and performs preprocessing, quality control, and subsequent analysis steps following state-of-the-art recommendations for scRNA-seq processing. The workflow removes low quality cells, assigns a cell type label to each cell, and visualizes the expression of individual genes of interest and functional pathways on the single cells. Moreover, in disease-related analyses scAmpi can link the observed gene expression to potential drug candidates that could be suited to treat the disease.
- Subjects
RNA sequencing; GENE expression; CELL analysis; DECISION making; CELL physiology; PIPELINE inspection; QUALITY control
- Publication
PLoS Computational Biology, 2022, Vol 18, Issue 6, p1
- ISSN
1553-734X
- Publication type
Article
- DOI
10.1371/journal.pcbi.1010097