Enrichment (or over-representation) Analysis from Scratch скачать в хорошем качестве

Enrichment (or over-representation) Analysis from Scratch

Disentangle where and how drug acts upon cells through gene expression. Learn to do enrichment analysis in the R language. Enrichment (or over-representation) analysis is used to find if a group of genes share a common biological theme (ontology) or pathway. E.g. where inside a cell a certain drug is causing changes. During this lecture we will analyze a microarray dataset from a Human Embryonic Kidney (HEK) 293 cell line exposed for 4 hours to neocarzinostatin. Enrichment analysis allows us disentangle where and how this drug acts on the HEK 293 cells. Code on GitHub: https://gist.github.com/DannyArends/7... Presentation on OneDrive: https://1drv.ms/b/s!AtYWSYRMmSHZh7pSr... Data from the paper: https://pubmed.ncbi.nlm.nih.gov/15892... GEO Dataset: https://www.ncbi.nlm.nih.gov/geo/quer... Gene Ontology Lecture:    • Gene Ontology and mRNA visualization (Bioi...   Thanks for taking an interest in my channel 😄If you've made it this far down, support me by giving a like or subscribing. 00:00:00 Sound check 00:01:20 Overview Of the Stream 00:05:27 Enrichment Analysis 00:08:40 Today's dataset 00:14:15 R packages from BioConductor 00:20:43 Return code pthread_create() is 22 00:23:01 Defining the sample AnnotatedDataFrame 00:28:04 Use R to download from Gene Expression Omnibus 00:35:46 Differentially Expressed Genes (DEGs) using affy & limma 00:44:59 Basic volcano plot in R code 00:56:22 What is Gene Ontology 01:02:58 Parsing Gene Ontology into a matrix 01:11:34 Creating an Gene Ontology annotation matrix 01:13:56 Enrichment analysis - The theory 01:21:08 Foreground & Background sets 01:25:16 Enrichment Analysis R code using phyper 01:31:39 Formatting the output 01:39:19 Future improvements to Enrichment analysis 01:43:27 End of stream Questions & Banter #bioinformatics #rstats #enrichment #analysis #rlanguage #pathways #geneexpression #pathwayORA