bioinformatics scientists. I hope your work goes smoothly well and be safe.
I'm stuck in trouble at the step of clustering.
Briefly, I downloaded fastq files from a published study (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE121861). There are 24 samples that come from 6 different mouse tumors. Each tumor consists of two biological replicates where one biological replicate includes two technical replicates. So, I ran the command "cellranger count", and generated 24 "filtered_feature_bc_matrix" files. For example, for the MC38, bio1-tech1, bio1-tech2, bio2-tech1, bio2-tech2 samples exist. FYI, These samples contain tumor cells as well as tumor-infiltrating immune cells.
And then, I carried out the command "cellranger aggr" to combine all matrix files into one matrix file. As suggested Seurat tutorial (https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html), Standard pre-processing workflow, linear dimensional reduction, determining dimensionality, clustering, and differential expression were done. Below are the commands I put.
tumors <- NormalizeData(tumors)
tumors <- FindVariableFeatures(tumors, selection.method = "vst", nfeatures = 2000)
all.genes <- rownames(tumors)
tumors <- ScaleData(tumors, features = all.genes)
tumors <- RunPCA(tumors, features = VariableFeatures(object = tumors))
tumors <- JackStraw(tumors, num.replicate = 100)
tumors <- ScoreJackStraw(tumors, dims = 1:20)
tumors <- FindNeighbors(tumors, dims = 1:10) # I chose dims from 5, 6, 7, 8, 9, 10 or 20 as a cutoff.
tumors <- FindClusters(tumors, resolution = 1.2)
tumors <- RunUMAP(tumors, dims = 1:10)
VlnPlot(tumors, features = c("Cd79a", "Foxp3", "Cd3e", "Cd4", "Zap70", "Cd8a", "Cd14", "Il2ra"))
Interestingly, as you can see here, Foxp3, Cd4, Cd8a are found to be overlapped. This phenomenon was the same when I drew vlnplot using different dimensionality from dims=1:5 to 1:10 or 1:20.
Next, I performed this single-cell RNA sequencing analysis using each tumor; mc38, b16, sa1n, ll2, emt6, and ct26. Representatively, MC38, SA1N, EMT6, and CT26 are presented as below.
So, except for SA1N that only separates Foxp3 from CD8 T cells, I found that other tumor types can't separate Fopx3 from CD8 T cells. Do you think it is related to technical issues, such as values of dimensionality or PCA I put? Since I am not an expert about scRNA, I don't know the exact meaning of dims or PCA, though. Is there anyone who can tell me what's going on? And, do you know how to make the right clustering to get exact T cell subpopulations?
I do appreciate your valuable time and thank you for reading this long story.