Single Cell Datasets Ep 2 - RNA quality control walkthrough on 10xGenomics dataset

Controlling RNA quality in a dataset prevents erroneous biological conclusions, saves money, and ensures data reproducibility. In this video, I walk through Quality Control (QC) for RNA-seq data applying core statistical learning concepts to real biological data. If you're working with RNA-seq experiments (or just curious how researchers check their data before running any analysis), this video breaks down what to look for and why it matters. Repo Link: https://github.com/sabadaftari/RNA2Pr... Timestamps: 00:00 - Recap 2:17 - RNA quality control 3:26 - Mark RNA mitochondrial percentage 4:38 - Calculate the quality control metrics 8:26 - Plot the total counts , number of genes by counts, and percentage mitochondrial counts 10:37 - Plot analysis 14:14 - Plot scatter plots one between total counts and number of genes per count and the other between total counts and percentage mitochondrial counts 16:57 - Scatter plots analysis 18:00 - Filter out RNA and protein values with low RNA signal 22:00 - Create a module and a function to hold the RNA quality control #MachineLearning #Biotech #Proteomics #RNAseq #Bioinformatics #Singlecell #dataanalyticscourse #statistics