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.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Kabir MH,Djordjevic D,O'Connor MD,Ho JWKdoi
10.1016/j.compbiolchem.2018.10.003subject
Has Abstractpub_date
2018-12-01 00:00:00pages
187-192eissn
1476-9271issn
1476-928Xpii
S1476-9271(18)30159-2journal_volume
77pub_type
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