bioRxivBioinformaticsPreprint
The Cell Subtypes Selection by Genes (CSSG) algorithm for discovering cell populations in high resolution
Abstract
The recent massive improvements in transcriptomics and single-cell technologies have led to a rising volume of data and demand for advances in bioinformatics processing. Existing methods are not fully capable of discovering genetic markers responsible for high-resolution cellular tissue heterogeneity, cell lineages during organism development, and cell differentiation with rare intermediate populations. In response to demand, we have generated a new Cell Subtypes Selection by Genes (CSSG) algorithm which is supported by a dedicated and fully automatic JSEQ(R) pipeline. The new CSSG algorithm is iterative, parallel, and able to make decisions for discovering cell populations in tissues based on transcript occurrence in cells. The CSSG/JSEQ is complemented by a new strategy and specialized algorithm for the naming of cell populations. Our approach allows for high-resolution tracing of cell populations, finding relations and hierarchy between them, particularly important for complex tissues such as the brain. The pipeline allows the establishment of developmental, differentiation, and pathogenic trajectory and takes a "snapshot" of a current physiological or pathological cellular stage of the investigated organ at the transcriptional level.
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: clustering
- research focus: cell type annotation
- research focus: trajectory inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 3
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