326 - Cell type annotation for single cell RNA seq data​

Описание: 326 - Cell type annotation for single-cell RNA-seq data

Code from this video is available here: https://github.com/bnsreenu/python_fo...

Previous video: Transcriptomics Unveiled – An In-Depth Exploration of Single Cell RNASeq Analysis using python:    • 325: Transcriptomics Unveiled – An In-Dept...  

GitHub link for the scsa library: https://github.com/bioinfo-ibms-pumc/...

Reference paper: Cao Y, Wang X and Peng G (2020) SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data. Front. Genet. 11:490. doi: https://doi.org/10.3389/fgene.2020.00490

https://www.frontiersin.org/articles/...

Description:

scRNA-seq permits comparison of the transcriptomes of individual cells ​that helps to assess transcriptional similarities and differences within a population of cells​. It also helps in identifying rare cell populations that would otherwise go undetected in analyses of pooled cells​. There are many techniques for scRNA-seq
including Visium, Slide-seq, SeqFISH, MERFISH, and Drop-seq. For all these techniques, the end result is a table that represents the gene expression profiles of individual cells.​

​The table typically consists of rows representing individual cells or spatial locations within the tissue and columns representing genes. The values in the table correspond to the gene expression intensities or counts for each cell or location.​ Downstream analysis includes, quality control, dimensionality reduction, clustering, differential expression analysis, cell type identification, spatial analysis, and visualization.

This video explains the process of cell type identification using the scsa library in python. Cell type annotation is the process of assigning or identifying the specific cell types or cell identities present in a biological sample, based on gene expression patterns. ​

The SCSA library allows for accurate cell type annotation by comparing scRNA-seq data to reference cell type profiles.​ It calculates specificity scores for each cell type, measuring the likelihood of a cell belonging to a specific cell type based on its gene expression profile.​ The library includes pre-built reference databases for various organisms, enabling cell type annotation in different biological contexts.​ Users can also create custom reference databases tailored to their specific experimental systems or incorporate external reference datasets.​

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