This project aims to create a purified integrated public porcin data as our reference to investigate and monitor our experimental muscle cells status, which further guide our lab experiments, including biomarker designing in qPCR, bulk rna celltype identification, etc., rearching out our ultimate goal of efficiently producing cultivated meat.
It is mainly About 3 packages applications and some customized functions in R.
Keyword: Single cell analysis, data integration, memory conversion into disk for large-scale datasets (memory reduction), visualization, bulk RNA cell-type prediction
library(Seurat) # SingleCell analysis. Link: https://satijalab.org/seurat/
library(monocle3) # Trajactory lineage analysis. Link: https://cole-trapnell-lab.github.io/monocle3/
library(BPCells) # Memory reduction. Link: https://github.com/bnprks/BPCells
library(BayesPrism) # Bulk RNA-seq prediction. Link: https://www.bayesprism.org
Human somitoid data link: From paper of Paraxial mesoderm organoids model development of human somites (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE194214)
Pig embryonic data link: From paper of Integrative single-cell RNA-seq and ATAC-seq analysis of myogenic differentiation in pig (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse206914)
Pig gastrulation data link: From paper of A single-cell atlas of pig gastrulation as a resource for comparative embryology (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE236766)
Note that data cover cell stages (time) from day1 to day4 (human somitoid cells in vitro) and from E11.5 to E28 (the last two datasets, porcine embryonic cells in vivo). These two categories data (human muscle & porcine) covered all cells of our interests (muscle related cells), and comprehensively demonstrated some deep information of cellular lineages. The code upload here is NOT polished.
Waiting for my spare time to finish this part...
