In steveyu323/SEURATEXT:

A Demonstration for Input with 4 different conditions for ShinyApp inspection based on injured/non-injured - KI/WT CD45 positive samples

Reading in required package and R scripts

library(dplyr)
library(Seurat)
library(purrr)
library(cowplot)
library(parallel)
library(roxygen2)
library(reshape2)
library(tibble)
source('../R/cell_cytometry.R')
source('../R/DE_analysis.R')

Parse in 10X Genomics Input

path1 = "../../MAP3K3/data/WNI45/"
path2 = "../../MAP3K3/data/KINI45/"
path3 = "../../MAP3K3/data/WTI45/"
path4 = "../../MAP3K3/data/KII45/"

wni = Read10X(data.dir = path1)
kini = Read10X(data.dir = path2)
wti = Read10X(data.dir = path3)
kii = Read10X(data.dir = path4)

Create Seurat Object

wni = CreateSeuratObject(counts = wni, project = 'wni', min.cells = 3, min.features = 200)

kini = CreateSeuratObject(counts = kini, project = 'kini', min.cells = 3, min.features = 200)

wti = CreateSeuratObject(counts = wti, project = 'wti', min.cells = 3, min.features = 200)

kii = CreateSeuratObject(counts = kii, project = 'kii', min.cells = 3, min.features = 200)

Specify the "stim" field in the object with a specific condition

wni$stim = "wni"

kini$stim = "kini"

wti$stim = "wti"

kii$stim = "kii"

Calculate the Mitochondria read percentage for pre-processing

wni[["percent.mt"]] <- PercentageFeatureSet(wni, pattern = "^MT-|^mt")

kini[["percent.mt"]] <- PercentageFeatureSet(kini, pattern = "^MT-|^mt")

wti[["percent.mt"]] <- PercentageFeatureSet(wti, pattern = "^MT-|^mt")

kii[["percent.mt"]] <- PercentageFeatureSet(kii, pattern = "^MT-|^mt")

Filter out abnormal cells Based on the mt percent distribution and nFeature distribution

wni <- subset(wni, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 20)

kini <- subset(kini, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 20)

wti <- subset(wti, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 20)

kii <- subset(kii, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & percent.mt < 20)

Normalize count table and find variable features for dimensionality reduction

wni <- NormalizeData(wni, normalization.method = "LogNormalize", scale.factor = 10000)
wni <- FindVariableFeatures(wni, selection.method = "vst", nfeatures = 2500)

kini <- NormalizeData(kini, normalization.method = "LogNormalize", scale.factor = 10000)
kini <- FindVariableFeatures(kini, selection.method = "vst", nfeatures = 2500)

wti <- NormalizeData(wti, normalization.method = "LogNormalize", scale.factor = 10000)
wti <- FindVariableFeatures(wti, selection.method = "vst", nfeatures = 2500)

kii <- NormalizeData(kii, normalization.method = "LogNormalize", scale.factor = 10000)
kii <- FindVariableFeatures(kii, selection.method = "vst", nfeatures = 2500)

Integrate all four samples together

immune.anchors <- FindIntegrationAnchors(object.list = list(wni, kini,wti,kii))

immune.combined <- IntegrateData(anchorset = immune.anchors)

Proceed to the anchored clustering with UMAP algorithm

DefaultAssay(immune.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)

# t-SNE and Clustering
immune.combined <- RunUMAP(immune.combined, reduction = "pca",dims = 1:10)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca",dims = 1:10)
immune.combined <- FindClusters(immune.combined, resolution = 0.6)

Save the Seurat Object with integration, normalization, and clustering information for later use

save(immune.combined, file= "../data/WTKICD45POS.RData")

Load the saved Object back for downstream analysis

load("../data/WTKICD45POS.RData")

Visualizing UMAP clustering

DimPlot(immune.combined, reduction = "umap", split.by = "stim",label = T)

Find The Conserve Markers for all clusters

conserved.markers <- Build.ConserveMarkers.All(immune.combined)

Find the differentially expressed gene with a pair of condition specified

diff.genes.wt = DE.Each.Cluster(immune.combined,pair = c("wni","wti"))
diff.gene.ki = DE.Each.Cluster(immune.combined,pair = c("kini","kii"))
diff.genes.i = DE.Each.Cluster(immune.combined,pair = c("wti","kii"))

# Combine the 3 different comparison into a list
diff.genes = list(diff.genes.wt = diff.genes.wt, diff.gene.ki = diff.gene.ki, diff.genes.i = diff.genes.i)

For the clusters within a condition find the representative gene of each cluster

markers.each = Find.Markers.Each(immune.combined,multi = c("kii","kini","wni","wti"))

Output the results for visualization in ShinyApp with multiple conditions

WTKICD45POS.out = Shine.Out(ob = immune.combined, diff = diff.genes, markers.each = markers.each, markers.conserved = conserved.markers)

saveRDS(object = WTKICD45POS.out,file = './ShinyDiff_multi/input/WTKICD45POS_out.rds')