Single Cell RNA-Seq Analysis in R With Seurat |ScRNA-seq Analysis | Bioinformatics for Beginners
Single-cell RNA sequencing (scRNA-seq) analysis in R using Seurat is a powerful method for studying gene expression at the individual cell level, enabling researchers to explore cellular heterogeneity and identify distinct cell populations. In this bioinformatics tutorial, we demonstrate how to perform a comprehensive analysis of single-cell RNA-seq data using Seurat in R. The dataset used for this walkthrough is the well-known 3k PBMCs (Peripheral Blood Mononuclear Cells) from 10x Genomics. Key steps covered in this video include: Loading Data: Use Read10X to load the single-cell data and CreateSeuratObject to create a Seurat object. Quality Control (QC): Filter cells based on the number of genes detected (min.features) and percentage of mitochondrial genes (PercentageFeatureSet). Normalization: Use NormalizeData to normalize the data and scale it using ScaleData. Feature Selection: Identify highly variable features using FindVariableFeatures. Dimensionality Reduction: Perform Principal Component Analysis (PCA) using RunPCA to reduce dimensionality. Visualize principal components using ElbowPlot and DimPlot. Clustering: Cluster the cells using FindNeighbors and FindClusters. Visualize clusters in UMAP or t-SNE space with RunUMAP and RunTSNE. Marker Gene Identification: Use FindAllMarkers to identify cluster-specific marker genes and visualize these with plots. Visualization Tools: DimHeatmap: Plot heatmaps of PCA dimensions to assess the quality of dimensional reduction. DoHeatmap: Generate heatmaps for identified clusters. FeaturePlot: Plot expression levels of selected marker genes. VlnPlot: Create violin plots to show the distribution of marker expression levels across clusters. Functions used in this tutorial: Read10X, CreateSeuratObject, PercentageFeatureSet, NormalizeData, FindVariableFeatures, ScaleData, RunPCA, ElbowPlot, FindNeighbors, FindClusters, RunUMAP, RunTSNE, FindAllMarkers, DimPlot, DimHeatmap, DoHeatmap, FeaturePlot, VlnPlot. By the end of this tutorial, you’ll be equipped with the tools needed to preprocess, analyze, and visualize single-cell RNA-seq data using the Seurat package in R. Links to Resources: Seurat Documentation:https://satijalab.org/seurat/articles... PBMC 3k Dataset: https://cf.10xgenomics.com/samples/ce... #mrbioinformatix #bioinformatics #bioinformaticsforbeginners #seurat #scrna #singlecellrnaseq #rnaseq #doheatmap #read10x #dimheatmap #singlecell Subscribe & Follow| TikTok Account: https://www.tiktok.com/@mrbioinformat... Instagram Account : https://www.instagram.com/mr.bioinfor...
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