r-scPipeline

A Wrapper for 'Seurat' and Related R Packages for End-to-End Single Cell Analysis.

scPipeline, a R package that uses Seurat, ReactomeGSA, fastMNN and singleR to enable the user to build an end-to-end single cell pipeline:

Seurat: A comprehensive toolkit for single-cell RNA-seq data analysis, offering functionalities from data preprocessing to visualization.

batchelor: Provides methods for batch correction in single-cell RNA-seq data, including the fastMNN function.

singleR: Facilitates single-cell RNA-seq data annotation, aiding in cell type identification.

GSEA: Enables gene set enrichment analysis to identify pathways or gene sets that are significantly enriched in a dataset.

Seurat wrapper to report expressed markers and associated Reactome pathways.

This repository is a simple R package that has 4 main functionalities:

1. SeuratPreprocess function to transform counts data to scaled seurat object,
2. SeuratLowDim function to convert the scaled object to low-dimensinoal object with cluster information,
3. SeuratMarkers function identifies the entire list of markers, along with significant markers (based on minimum percent of cells) and
4. ReactomeData function to identify the Reactome GSA pathways on the expressed genes in the clusters.

Additonal advanced functionalities of transferring cell-annotations, and identifying the annotations using SingleR package are also available. 5. ConvertGeneIdentifiers to convert different accessions to gene symbols or vice versa. 6. AnnotateCellsWithSingleR uses celldex reference annotations to transfer to the current dataset. 7. Transfer Annotations uses the labelled datasets from one dataset and transfers to the other.

library(Seurat)
library(ReactomeGSA)
library(tidyverse)
library(scPipeline)

# Loading sample counts data (Mouse cell atlas)
mca.matrix <- readRDS(file = "data/MCA_merged_mat.rds")
mca.metadata <- read.csv(file = "data/MCA_All-batch-removed-assignments.csv", row.names = 1)
mca.matrix.1K <- mca.matrix[,1:1000]

[1]. SeuratPreprocess Function to convert Counts Data to Normalized and Scaled Seurat object for highly variable genes.

scaled_seurat_object <- SeuratPreprocess(mca.matrix.1K)

[2]. SeuratLowDim Function to convert Scaled Seurat Object from [1] to object that has clusters identified and data transformed to visualize in 2d (ie, PCA followed by t-SNE and UMAP).

low_dim_object <- SeuratLowDim(scaled_seurat_object)

[3]. SeuratMarkers Function to convert seurat object to all markers list, along with significant markers (based on minimum percent of cells in the cluster).

Markers_list <- SeuratMarkers(low_dim_object)

[4]. ReactomeData Function to convert seurat object to get the pathways identified using ReactomeGSA R package.

Reactome_pathways_object <- ReactomeData(low_dim_object)