Run Principal Component Analysis

Run Principal Component Analysis — RunPCA • Seurat

Tips for integrating large datasets • Seurat - Satija Lab

Create a list of Seurat objects to integrate · Perform normalization, feature selection, and scaling separately for each dataset · Run PCA on each ...

Benchmark principal component analysis (PCA) of scRNA-seq data ...

Introduction to PCA and SVD · Compare 5 functions for principal component analysis · Load single-cell RNA-seq data · Run PCA or SVD with each ...

Using sctransform in Seurat - Satija Lab

Perform dimensionality reduction by PCA and UMAP embedding · pbmc[["SCT"]]$scale. · To assist with visualization and interpretation, we also convert Pearson ...

How to perform PCA on single-cell RNA-Seq data in ... - YouTube

My Twitter: https://twitter.com/flo_compbio Savannah Bertrand's fundraiser: ...

Seurat Command List - Satija Lab

Visualization in Seurat ; # Dimensional reduction plot ; DimPlot(object = pbmc, reduction = "pca") ; # Dimensional reduction plot, with cells colored by a ...

Analysis, visualization, and integration of spatial datasets with Seurat

Gene expression visualization ... In Seurat, we have functionality to explore and interact with the inherently visual nature of spatial data. The ...

Seurat CCA? It's just a simple extension of PCA! - Xinming Tu

The canonical correlation analysis (CCA) implemented as part of Seurat software package is one of the most popular methods for batch effects ...

Function reference • Seurat - Satija Lab

Run Independent Component Analysis on gene expression. RunPCA(). Run Principal Component Analysis. RunSLSI(). Run Supervised Latent Semantic Indexing. RunSPCA ...

Quickly Pick Relevant Dimensions — ElbowPlot • Seurat - Satija Lab

Plots the standard deviations (or approximate singular values if running PCAFast) of the principle components for easy identification of an elbow in the graph.

Run UMAP — RunUMAP • Seurat - Satija Lab

# S3 method for Seurat RunUMAP( object, dims = NULL, reduction = "pca", features = NULL, graph = NULL, assay = DefaultAssay(object = object), nn.name = NULL, ...

Guided Clustering of the Microwell-seq Mouse Cell Atlas - Satija Lab

Dimensional Reduction (PCA). mca <- RunPCA(mca, npcs = 100, ndims.print = 1:5, nfeatures.

Seurat Object Explained: Beginner's Guide and Demo - YouTube

... Seurat object for single-cell RNA sequencing data analysis, with a hands-on demonstration in RStudio ... Principal Component Analysis (PCA). Steve ...

Integration and Label Transfer - Satija Lab

Here we scale the integrated data, run PCA, and visualize the results with UMAP. ... First, setup the Seurat object list, and run SCTransform on ...

Cell-Cycle Scoring and Regression - Satija Lab

If we run a PCA on our object, using the variable genes we found in ... PCA or downstream analysis. marrow <- RunPCA(marrow, features ...

Seurat - Guided Clustering Tutorial - Satija Lab

We next use the count matrix to create a Seurat object. The object serves as a container that contains both data (like the count matrix) and analysis (like PCA, ...

Harmony Integration — HarmonyIntegration • Seurat - Satija Lab

Integrative analysis in Seurat v5 · Mapping and annotating query datasets ... RunPCA(obj) # After preprocessing, we integrate layers with added ...

Seurat - Guided Clustering Tutorial - Satija Lab

Next we perform PCA on the scaled data. By default, the genes in [email protected] are used as input, but can be defined using pc.genes. We have ...

Cell-Cycle Scoring and Regression - Satija Lab

If we run a PCA on our object, using the variable genes we found in ... PCA or downstream analysis. marrow <- RunPCA(object = marrow ...