Having more than one sample

[beginner984]

Hi immunarch team

I have 10x TCR and BCR sequencing + 5' GEX

I have several samples

For one sample I know I should do like this

> setwd("/Users/angel/Downloads/p1_sample_feature_bc_matrix")
> list.files()
[1] "filtered_contig_annotations.csv"
> file_path =getwd()
> immdata_10x <- repLoad(file_path)

== Step 1/3: loading repertoire files... ==

Processing "/Users/angel/Downloads/p1_sample_feature_bc_matrix" ...
  -- [1/1] Parsing "/Users/angel/Downloads/p1_sample_feature_bc_matrix/filtered_contig_annotations.csv" -- 10x (filt.contigs)
                                                                                                                     
== Step 2/3: checking metadata files and merging files... ==

Processing "/Users/angel/Downloads/p1_sample_feature_bc_matrix" ...
  -- Metadata file not found; creating a dummy metadata...

== Step 3/3: processing paired chain data... ==

Done!

Warning messages:
1: The following named parsers don't match the column names: barcode,is_cell,contig_id,high_confidence,length,chain,v_gene,d_gene,j_gene,c_gene,full_length,productive,fwr1,fwr1_nt,cdr1,cdr1_nt,fwr2,fwr2_nt,cdr2,cdr2_nt,fwr3,fwr3_nt,cdr3,cdr3_nt,fwr4,fwr4_nt,reads,umis,raw_clonotype_id,raw_consensus_id,exact_subclonotype_id 
2: In for (i in seq_len(n)) { :
  closing unused connection 3 (/Users/angel/Downloads/0001.png)

From 5' GEX data I have made a Seurat object, how I can merge Seurat with immdata_10x object"? Thanks for an y help

[sumedharoy]

To add to the question above: I am also working with scTCRseq and scBCRseq data. I am able to read in multiple sample files with a function, but I am not able to run any analysis as the expected "CD3.AA" etc column names are different by default in CellRanger processed files. The ability to merge a seurat object would be great, but I think I can find a workaround that, to be ultimately be able to do analysis of specific T cell subsets called by Seurat. Any updated guidelines on the single-cell pipeline would be much appreciated! Thank you!