FAQ

How does TCRconvert work?

TCRconvert performs a merge between the input data and a lookup table that includes gene names with each nomenclature. These lookup tables are constructed from IMGT reference FASTA files and account for the specific naming peculiarities of each platform. The built-in lookup tables are located under tcrconvert/data/. The code used to build the lookup tables, which demonstrates the conversion logic, is within the <build_lookup_from_fastas https://github.com/seshadrilab/tcrconvert/blob/361f930d598996f3fa239a715c829ad8c107c63e/tcrconvert/build_lookup.py#L196>_ function.

What input columns are required?

TCRconvert expects at least one V, D, J, and/or C gene column. You can use standard 10X and Adaptive column names or custom names.

What if I have missing genes?

NA values in the input dataframe will remain NA in the output. Genes that are not found in the lookup table (which is based on the IMGT reference), will be converted to NA. The built-in lookup tables are located under tcrconvert/data/.

Are gamma-delta TCRs supported?

Yes, for human, mouse, and rhesus macaque.

How are alleles added from 10X data?

Since 10X does not provide allele-level information, all genes are assigned the allele *01.

How are C genes converted to Adaptive?

Adaptive does not capture constant (“C”) gene information. If converting to the Adaptive format, all C genes will be set to NA.

What column names should I use for my IMGT-formatted data?

IMGT does not have standard column names, so it’s assumed that the 10X names are used: (v_gene, d_gene, j_gene, c_gene). To use other names, specify them as a list with frm_cols (library) or --column (command-line).

Can I input AIRR files?

Yes, just specify the AIRR column names (v_call, d_call, j_call, c_call) using frm_cols (library) or --column (command-line). You must still specify the input naming convention with frm.

What if I have custom column names?

If you’re using non-standard column names that do not match 10X, Adaptive, or Adaptive V2 formats, specify them with frm_cols (library) or --column (command-line).

What about odd names (e.g. `TRAV14DV4`, `TCRAV01-02/12-02`)?

Gene names containing “OR” or “DV” are accounted for in the lookup tables.

Combinations of gene names, like TCRAV01-02/12-02, will be converted to NA because they are not in the IMGT reference.

Are non-human species supported?

Mouse and rhesus macaque are supported out-of-the-box. For other species, see pages for building custom lookup tables for library or command-line usage.

The rhesus and mouse lookup tables were built from IMGT reference FASTAs and gene tables. Mouse genes cover both “Mouse” and “Mouse C57BL/6J” as listed in IMGT.

What if my Adaptive data lacks `x_resolved` / `xMaxResolved` columns?

Create them by combining x_gene/xGeneName and x_allele/xGeneAllele with * as a separator. Example code:

import pandas as pd
import numpy as np

def create_col(gene_col, allele_col):
    return np.where(allele_col.notna(), gene_col + "*" + allele_col, gene_col)

new_df = adaptive_df.copy()

# Adaptive
new_df['v_resolved'] = create_col(new_df['v_gene'], new_df['v_allele'])
new_df['d_resolved'] = create_col(new_df['d_gene'], new_df['d_allele'])
new_df['j_resolved'] = create_col(new_df['j_gene'], new_df['j_allele'])

# Adaptive v2
new_df['vMaxResolved'] = create_col(new_df['vGeneName'], new_df['vGeneAllele'])
new_df['dMaxResolved'] = create_col(new_df['dGeneName'], new_df['dGeneAllele'])
new_df['jMaxResolved'] = create_col(new_df['jGeneName'], new_df['jGeneAllele'])