Is my code using Seurat correctly?

Question

[shweta.johari13]

Hello,

My name is Shweta Johari and I have 4 datasets and I have run it on Seurat using the integration vignette.

My questions are as follows:

1. Is the running of the code correct?
2. Now that I have the clusters, what to do?

library(Seurat)
library(patchwork)

control = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu1_099")
inhibitor = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu2_099")
dnavaccine = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu3_099")
combitr = Read10X("C:/Users/TVIT1/Desktop/imtegrationsdj_Tuall/Tu4_099")

# Created the Seurat Object:
control_seurat = CreateSeuratObject(counts = control$Gene Expression, project = "control")
inhibitor_seurat = CreateSeuratObject(counts = inhibitor$Gene Expression, project = "inhibitor")
dnavaccine_seurat = CreateSeuratObject(counts = dnavaccine$Gene Expression, project = "dnavaccine")
combitr_seurat = CreateSeuratObject(counts = combitr$Gene Expression, project = "combitr")

# Merged the datasets
merge_seurat = merge(x = control_seurat, y = c(inhibitor_seurat, dnavaccine_seurat, combitr_seurat), add.cell.id = c("control", "inhibitor","dnavaccine", "combitr"))
head([e-mail],5)

# Filtering the mitochondrial genes
merge_seurat$mitoRatio <- PercentageFeatureSet(object = merge_seurat, pattern = "^mt-")
metadata <- [e-mail]
metadata$cells <- rownames(metadata)
library(dplyr)
metadata <- metadata %>%
dplyr::rename(group = orig.ident,
nUMI = nCount_RNA,
nGene = nFeature_RNA)
library(stringr)
metadata$sample <- NA
metadata$sample[which(str_detect(metadata$cells, "^control_"))] <- "control"
metadata$sample[which(str_detect(metadata$cells, "^inhibitor_"))] <- "inhibitor"
metadata$sample[which(str_detect(metadata$cells, "^dnavaccine_"))] <- "dnavaccine"
metadata$sample[which(str_detect(metadata$cells, "^combitr_"))] <- "combitr"
[e-mail] <- metadata
head([e-mail],5)
tail([e-mail],5)
data.list <- SplitObject(merge_seurat, split.by = "sample")

data.list <- lapply(X = data.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst")
})

# Integration of datasets
features <- SelectIntegrationFeatures(object.list = data.list)
immune.anchors <- FindIntegrationAnchors(object.list = data.list, anchor.features = features)
immune.combined <- IntegrateData(anchorset = immune.anchors)
DefaultAssay(immune.combined) <- "integrated"
immune.combined <- ScaleData(immune.combined, verbose = FALSE)
immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:10)
immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30)
immune.combined <- FindClusters(immune.combined, resolution = 0.5)
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "sample")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE, repel = TRUE)
DimPlot(immune.combined, reduction = "umap", split.by = "sample")

DefaultAssay(immune.combined) <- "RNA"
library(metap)
all.markers084 <- FindAllMarkers(immune.combined, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

sixteen.markers084 <- FindConservedMarkers(immune.combined, ident.1 = 16, grouping.var = "sample", verbose = FALSE)

Questions again:

• Is this correct?
• What next?

[ATpoint]

if you follow the vignette then there is a good chance that you do a reasonable analysis. For the "what to do" question, it helps to think about your analysis goal first. So what is the question you want to answer?