C3: An R package for cross-species compendium-based cell-type identification. (December 2018)

• Record Type: Journal Article
• Authors: Kabir, Md Humayun; Djordjevic, Djordje; O'Connor, Michael D.; Ho, Joshua W.K.
• Is Part Of: Computational biology and chemistry. Volume 77(2018)
• Page Start: 187
• Page End: 192
• Publication Date: 2018-12
• Subjects: Bioinformatics -- Transcriptomics -- Cell type identification -- Cross-species -- Gene set analysis; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.10.003
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Compendium-based analyses for non-mouse/non-human species are challenging due to limited public gene expression data sets. We present C3 – an open source R package for cross-species compendium-based cell-type identification. C3 enables cross-species gene set analysis by using XGSA to correct for biases arising from complex homologous gene mapping. Our evaluation shows that C3 is a simple yet effective method for cell type identification for non-model organisms. 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 athttps://github.com/VCCRI/C3 .