Dimension Reduction Plot for Integrated Data

[tdfy]

Hello,

I have recently followed adopted the Harvard Chan Bioinformatics Core guidelines for SC QC/Normalization/Clustering (https://hbctraining.github.io/scRNA-seq_online/schedule/links-to-lessons.html). I have integrated CD4+/CD8+ T cells from two time points.

I recently received feedback that my integrated dimension reduction plot clustering looked problematic. Specifically, the small clusters peripheral (splash/star?<-- see cluster 7) and the number of distinct clusters.

Data was normalized using SCTransform, variables regressed were mitochondrial ratio and G2M-S phase score difference, as suggested for differentiating cell types. Alternative Workflow: https://satijalab.org/seurat/articles/cell_cycle_vignette.html

My clusters were called at 40 PC's w/ 0.6 resolution.

As for the number of clusters, TCR B VDJ subgenes were identified as strong conserved markers in several clusters. I wonder if it is worth excluding VDJ markers from analysis?

Any comment on the appearance of the dim plot and implications would be appreciated. Thank you!

split_seurat <- SplitObject(seurat_phase, split.by = "sample")

split_seurat <- split_seurat[c("samp1_rep1","samp2_rep2","samp3")]



for (i in 1:length(split_seurat)) {
   split_seurat[[i]] <- SCTransform(split_seurat[[i]], vars.to.regress = c("celldif","mitoRatio"))
 }

saveRDS(split_seurat,file= "split_seurat.rds")

##Second script###

split_seurat <- readRDS("split_seurat.rds")

integ_features <- SelectIntegrationFeatures(object.list = split_seurat,
                                            nfeatures = 3000)

# Prepare the SCT list object for integration
split_seurat <- PrepSCTIntegration(object.list = split_seurat,
                                   anchor.features = integ_features)

# Find best buddies - can take a while to run
integ_anchors <- FindIntegrationAnchors(object.list = split_seurat,
                                        normalization.method = "SCT",
                                        anchor.features = integ_features)

# Integrate across conditions
seurat_integrated <- IntegrateData(anchorset = integ_anchors,
                                   normalization.method = "SCT")