scholarly journals scHLAcount: allele-specific HLA expression from single-cell gene expression data

2020 ◽  
Vol 36 (12) ◽  
pp. 3905-3906 ◽  
Author(s):  
Charlotte A Darby ◽  
Michael J T Stubbington ◽  
Patrick J Marks ◽  
Álvaro Martínez Barrio ◽  
Ian T Fiddes
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Human Leukocyte ◽  
Supplementary Information ◽  
Hla Genes ◽  
Sequencing Studies ◽  
Cell Gene Expression ◽  
Allele Specific ◽  
Cell Gene

Abstract Summary Bulk RNA sequencing studies have demonstrated that human leukocyte antigen (HLA) genes may be expressed in a cell type-specific and allele-specific fashion. Single-cell gene expression assays have the potential to further resolve these expression patterns, but currently available methods do not perform allele-specific quantification at the molecule level. Here, we present scHLAcount, a post-processing workflow for single-cell RNA-seq data that computes allele-specific molecule counts of the HLA genes based on a personalized reference constructed from the sample’s HLA genotypes. Availability and implementation scHLAcount is available under the MIT license at https://github.com/10XGenomics/scHLAcount. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-cell gene expression patterns in lupus monocytes independently indicate disease activity, interferon and therapy

2017 ◽  
Vol 4 (1) ◽  
pp. e000202 ◽  
Author(s):  
Zhongbo Jin ◽  
Wei Fan ◽  
Mark A Jensen ◽  
Jessica M Dorschner ◽  
George F Bonadurer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Activity ◽  
Single Cell ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

2021 ◽  
Author(s):  
Kun Qian ◽  
Shiwei Fu ◽  
Hongwei Li ◽  
Wei Vivian Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Real Data ◽  
Heterogeneous Data ◽  
Biological Processes ◽  
Cell Gene Expression ◽  
Improved Performance ◽  
Cell Gene ◽  
Clinical Problems

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Even though different batch effect removal methods have been developed, none of the existing methods is suitable for heterogeneous single-cell samples coming from multiple biological conditions. To address this challenge, we propose a method named scINSIGHT to learn coordinated gene expression patterns that are common among or specific to different biological conditions, offering a unique chance to identify cellular identities and key biological processes across single-cell samples. We have evaluated scINSIGHT in comparison with state-of-the-art methods using simulated and real data, which consistently demonstrate its improved performance. In addition, our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

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