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

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.

Stems cells, big data and compendium-based analyses for identifying cell types, signalling pathways and gene regulatory networks

“…In comparison, a new open source R package developed by our group, termed C3, allows crossspecies identification of any cell type. C3 uses a large transcriptomic profile rather than a limited gene list, and is compatible with a wide variety of input compendia (Kabir et al 2018a). The cross-species comparison enabled by C3 makes use of a recently developed cross-species gene set analysis method called XGSA (Djordjevic et al 2016).…”

Cell type matching across species using protein embeddings and transfer learning

“…While this is a straightforward approach, it ignores genes with a more complex evolutionary history which might have caused divergent functional specification of cell types between species. The second group of methods, including SAMap (Tarashansky et al, 2021), CAME (Liu et al, 2021), Kmermaid (Botvinnik et al, 2021), and C3 (Kabir et al, 2018), overcomes this limitation by considering many-to-many relationships between the genes based on sequence similarity. All these methods rely on the classical assumption that sequence similarity is a good measure of how genes functionally relate to each other.…”