I have output from a single cell sequencing run that has both the VDJ and gene expression data. For the same cells, we also used a hybrid capture approach to sequence the TCR sequences. I have compared the TCR sequences across the two approaches and I have found a list of TRA and TRB cdr3 sequences that overlap. For the TRA sequences, there are 101 cells that were identified in both approaches. From here, I would like to use dimplot to look at a UMAP of where these cells fall in the clustering.
I have used the code below to integrate the vdj and gene expression data:
tcr <- read.csv(paste("/Users/carlygraham/Dropbox/BramsonLab/scRNAseq-Feb16/Multi_TAC_Output_v2/vdj/", "filtered_contig_annotations.csv", sep="")) tcr <- tcr[!duplicated(tcr$barcode), ] # Only keep the barcode and clonotype columns. # We'll get additional clonotype info from the clonotype table. tcr <- tcr[,c("barcode", "raw_clonotype_id")] names(tcr)[names(tcr) == "raw_clonotype_id"] <- "clonotype_id" # Clonotype-centric info. clono <- read.csv(paste("/Users/carlygraham/Dropbox/BramsonLab/scRNAseq-Feb16/Multi_TAC_Output_v2/vdj/","clonotypes.csv", sep="")) # Slap the AA sequences onto our original table by clonotype_id. tcr <- merge(tcr, clono[, c("clonotype_id", "cdr3s_aa")]) # Reorder so barcodes are first column and set them as rownames. tcr <- tcr[, c(2,1,3)] rownames(tcr) <- tcr[,1] tcr[,1] <- NULL # Add to the Seurat object's metadata. scRNAseq.seurat <- AddMetaData(object=scRNAseq.seurat, metadata=tcr)
This effectively gives me a seurat object with the cdr3 as metadata
From here is there a way to pull out some of the cells and display them on the UMAP?
Ex. pull out the cells with TRA:CAYGPPPAGNMLTF and TRA:CAAREGDKIIF?
Read more here: Source link