In fpestana-git/clusteringR: Transcriptomic clustering functions

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Install packages

devtools::install_github("fpestana-git/clusteringR",force = F)
devtools::install_github("fpestana-git/visualisR",force = F)

library(clusteringR)
library(dplyr)
library(Seurat)
library(visualisR)
library(ggplot2)
library(rliger)
library(ggpubr)
library(viridis)

Load data

# Load the PBMC dataset
pbmc <- Read10X(data.dir = "../data/pbmc/")

Clustering

# Run Seurat clustering using log normalization
seuratLOG <- clusteringSeurat(datasetObject = pbmc,datasetName = "test",metadataAvailable = F,mapTypeValue = "umap",normalizationMethod = "LOG")

# Run Seurat clustering using SCT normalization
seuratSCT <- clusteringSeurat(datasetObject = pbmc,datasetName = "test",metadataAvailable = F,mapTypeValue = "umap",normalizationMethod = "SCT")

# Run LIGER clustering using iNMF algorythm
ligerClustering <- clusteringLiger(datasets = list(pbmc = pbmc),referenceDatasetName = "pbmc", useiNMF = T)

Visualize Dim plot

drawDimPlot(seuratObject = seuratLOG,datasetName = "pbmc")
drawDimPlot(seuratObject = seuratSCT,datasetName = "pbmc")

Visualize Feature plot

drawFeaturePlot(seuratObject = seuratLOG,
                featureValues = c("S100A9","NKG7","LDHB","CD79A"),
                nrowValue = 2,
                ncolValue = 2,
                datasetName = "test")

Visualize Heatmap

# Visualize the variable features
drawHeatmapPlot(seuratObject = seuratLOG,featureNames = c("S100A9","NKG7","LDHB","CD79A"),assaytype = "RNA",plotName = "test",groupValue = "seurat_clusters",drawLinesValue = T)

Visualize Dot plot

drawDotPlot(seuratObject = seuratLOG,plotName = "test",featureValues = c("S100A9","NKG7","LDHB","CD79A"))

fpestana-git/clusteringR documentation built on May 3, 2022, 11:59 a.m.