How to analyze single-cell RNA-Seq data in R | Detailed Seurat Workflow Tutorial скачать в хорошем качестве

How to analyze single-cell RNA-Seq data in R | Detailed Seurat Workflow Tutorial

A detailed walk-through of standard workflow steps to analyze a single-cell RNA sequencing dataset from 10X Genomics in R using the #Seurat package. I hope you liked the video. I look forward to your comments under the comments section! Link to 10X dataset: https://www.10xgenomics.com/resources... (Gene Expression - Feature / cell matrix HDF5 (raw)) Link to code: https://github.com/kpatel427/YouTubeT... Chapters: 0:00 Intro 1:52 Download data from 10X Genomics website 4:16 Read counts matrix 6:33 Create a Seurat Object 7:53 Quality Control 15:36 Filtering 16:22 Normalization 17:33 '@commands' slot 18:38 Find Variable Features 21:22 Scale data 23:51 Difference between @counts, @data and @scale.data slots 24:58 Linear dimensionality reduction (PCA) 27:35 Determine the dimensionality of the dataset 29:32 Clustering 30:33 Understanding 'Resolution' in Clustering 34:48 Non-linear dimensionality reduction (UMAP) Show your support and encouragement by buying me a coffee: https://www.buymeacoffee.com/bioinfor... To get in touch: Website: https://bioinformagician.org/ Github: https://github.com/kpatel427 Email: [email protected] #bioinformagician #bioinformatics #seurat #R #genomics #beginners #tutorial #howto #omics #research #biology #ncbi #GEO #rnaseq #ngs