TY - JOUR
T1 - Alona
T2 - A web server for single-cell RNA-seq analysis
AU - Franzén, Oscar
AU - Björkegren, Johan L.M.
N1 - Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press.
PY - 2020/3/31
Y1 - 2020/3/31
N2 - Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes.
AB - Single-cell RNA sequencing (scRNA-seq) is a technology to measure gene expression in single cells. It has enabled discovery of new cell types and established cell type atlases of tissues and organs. The widespread adoption of scRNA-seq has created a need for user-friendly software for data analysis. We have developed a web server, alona that incorporates several of the most popular single-cell analysis algorithms into a flexible pipeline. alona can perform quality filtering, normalization, batch correction, clustering, cell type annotation and differential gene expression analysis. Data are visualized in the web browser using an interface based on JavaScript, allowing the user to query genes of interest and visualize the cluster structure. alona accepts a compressed gene expression matrix and identifies cell clusters with a graph-based clustering strategy. Cell types are identified from a comprehensive collection of marker genes or by specifying a custom set of marker genes.
UR - http://www.scopus.com/inward/record.url?scp=85087320147&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btaa269
DO - 10.1093/bioinformatics/btaa269
M3 - Article
C2 - 32324845
AN - SCOPUS:85087320147
SN - 1367-4803
VL - 36
SP - 3910
EP - 3912
JO - Bioinformatics
JF - Bioinformatics
IS - 12
ER -