C3: An R package for cross-species compendium-based cell-type identification

Md Humayun Kabir; Djordje Djordjevic; Michael D O'Connor; Joshua W K Ho

doi:10.1016/j.compbiolchem.2018.10.003

C3: An R package for cross-species compendium-based cell-type identification

Comput Biol Chem. 2018 Dec:77:187-192. doi: 10.1016/j.compbiolchem.2018.10.003. Epub 2018 Oct 9.

Authors

Md Humayun Kabir¹, Djordje Djordjevic², Michael D O'Connor³, Joshua W K Ho⁴

Affiliations

¹ School of Medicine, Western Sydney University, Campbelltown, NSW, Australia; Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia; Department of Computer Science and Engineering, University of Rajshahi, Bangladesh.
² Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia; St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia.
³ School of Medicine, Western Sydney University, Campbelltown, NSW, Australia; Medical Sciences Research Group, Western Sydney University, Campbelltown, NSW, Australia.
⁴ Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia; St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia; School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China. Electronic address: jwkho@hku.hk.

PMID: 30340080
DOI: 10.1016/j.compbiolchem.2018.10.003

Abstract

Cell type identification from an unknown sample can often be done by comparing its gene expression profile against a gene expression database containing profiles of a large number of cell-types. This type of compendium-based cell-type identification strategy is particularly successful for human and mouse samples because a large volume of data exists for these organisms. However, such rich data repositories often do not exist for most non-model organisms. This makes transcriptome-based sample classification in these species challenging. We propose to overcome this challenge by performing a cross-species compendium comparison. The key is to utilise a recently published cross-species gene set analysis (XGSA) framework to correct for biases that may arise due to potentially complex homologous gene mapping between two species. The framework is implemented as an open source R package called C3. We have evaluated the performance of C3 using a variety of public data in NCBI Gene Expression Omnibus. We also compared the functionality and performance of C3 against some similar gene expression profile matching tools. Our evaluation shows that C3 is a simple and effective method for cell type identification. C3 is available at https://github.com/VCCRI/C3.

Keywords: Bioinformatics; Cell type identification; Cross-species; Gene set analysis; Transcriptomics.

MeSH terms

Animals
Cells / metabolism*
Databases, Genetic
Gene Expression Profiling*
Humans
Organ Specificity / genetics*
Software*