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

326 - Cell type annotation for single-cell RNA-seq data
Code from this video is available here: github.com/bnsreenu/python_fo...
Previous video: Transcriptomics Unveiled - An In-Depth Exploration of Single Cell RNASeq Analysis using python: • 325: Transcriptomics U...
GitHub link for the scsa library: 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: doi.org/10.3389/fgene.2020.00490
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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[@27medo3]

Very nice tutorial, Thank you for the thorough tutorial, waiting for the next video

[@ghanendrasingh4033]

Absolutely an amazing video Sreenivas B. Highly useful for non biologists and even for computational biologists or those learning bioinformatics.
Would be great if you can cover similar videos. Looking forward to it. :)

[@elizabeththomasucc_e-learn3754]

Thank you for the video. It's presented in a simple way and non biologists can also understand. Good work !! Please could you explain about how to get cell to cell interactions from spatial single cell data

[@viveksharma-uh2nc]

Nice tutorial. Thank you. I get the following error when I run it on my dataset
KeyError: 'GO:0062103'
Any clue on how to fix it?
I tried both whole.db and whole_v2.db without any luck