README.md

In basilkhuder/Beginnner_Single_Cell_Workshop: scRNA-Seq Workshop Package

Install and load the Workshop Package

install.packages("devtools")
devtools::install_github("basilkhuder/Beginnner_Single_Cell_Workshop")
library("Beginner_Single_Cell_Workshop")

Download the PBMC3K Dataset

pbmc <- install_workshop_data("pbmc")

Create a Seurat object

pbmc <- CreateSeuratObject(data = pbmc, min.cells = 3, min.features = 200)

What do the min.cells and min.features parameters do?

min.cells helps limit the number of genes used by removing those unlikely to play any part in differentiating groups of cells due to being expressed in very few cells. In general, most genes removed will be those with zero counts across all cells. min.features removes dead cells cells and empty droplets where few genes are detected. Source: https://www.biostars.org/p/407339/

Produce Quality Control Visualization

produceQCVisualization(pbmc)

Filter the Seurat Object

pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)

Process the Seurat Object

pbmc <- processSeurat(pbmc, dims = 1:50, cluster.res = .5, vars_to_regress = "percent.mt") 

Visualize Cell Clusters

DimPlot(pbmc)

Predict Cluster Cell Identities

predictClusters(pbmc, assign.to.object = FALSE, print.predictions = TRUE)
second_predictions <- findmarkergenes(pbmc, species = "human", cluster = 'All')
scCATCH(second_predictions, species = "human")                

Compare Predictions to Real Assignment

Assign Predicted Identities to Object

predictClusters(pbmc, assign.to.object = TRUE, print.predictions = fALSE)

Visualize with Labeled Clusters

basilkhuder/Beginnner_Single_Cell_Workshop documentation built on Sept. 21, 2020, 12:02 a.m.