TCRcloud on Pypi

TCRcloud is an Adaptive Immune Receptor Repertoire (AIRR) visualization and comparison tool

Instalation

TCRcloud is written in python and can be installed from PyPI using pip:

pip3 install TCRcloud

It is compatible with Linux and macOS operating systems and on Windows through the Windows Subsystem for Linux.

TCRcloud uses the AIRR Data Commons API and needs AIRR compliant data as input.

TCRcloud was initially developed for TCR repertoires but it is also compatible with BCR repertoires.

Usage

To create a word cloud

TCRcloud cloud repertoire.airr.rearrangements.tsv

By default TCRcloud colours the CDR3 based on the V gene. Only the colours for human TCR and BCR variable genes are coded into TCRcloud but you can provide a json file that atributes colours in Hex format to specific sequences:

{
"#FF0000":["CAVSLPTDSWGKLQF","CASSLVVADPYQETQYF"],
"#0000FF":["CAYRSKGSQGNLIF","CASSLGGQSGNEQFF"]
}

The sequences not in the json file will be coloured grey.

To use your custom colours for the word cloud

TCRcloud cloud repertoire.airr.rearrangements.tsv -c colours.json

To create a word cloud without a legend

TCRcloud cloud repertoire.airr.rearrangements.tsv -l False

To create a radar plot comparing diversity indices

TCRcloud radar repertoire.airr.rearrangements.tsv

By default TCRcloud uses repertoire_id but you can create a legend with the text you want by providing a json file:

{
"PRJNA509910-su008_pre-TRA":"Subject 8 pre-treatment",
"PRJNA509910-su008_post-TRA":"Subject 8 post-treatment",
"PRJNA509910-su008_pre-TRB":"Subject 8 pre-treatment",
"PRJNA509910-su008_post-TRB":"Subject 8 post-treatment"
}

To create a radar plot with your desired legend

TCRcloud radar repertoire.airr.rearrangements.tsv -c legend.json

To create a radar plot without a legend

TCRcloud radar repertoire.airr.rearrangements.tsv -l False

To export the calculated indices from the radar to a text file

TCRcloud radar repertoire.airr.rearrangements.tsv -e True

The indices utilized in the radar

Distinct CDR3: Count of the unique CDR3 sequences in the sample

Convergence: Frequency of CDR3 amino acid sequences that are coded by more than one nucleotide sequence

D50 Index: Developed by Dr Jian Han, this index represents the percent of dominant and unique T or B cell clones that account for the cumulative 50% of the total CDR3 counted in the sample

Gini Coefficient: Originally developed by Dr Corrado Gini to represent the wealth inequality within a social group

Shannon Index: Originally developed by Dr Claude Shannon to quantify the entropy in strings of text. Calculated here using log₂

Gini-Simpson Index: This is a transformation of the index originally developed by Dr Edward H. Simpson to measure diversity in a ecosystem

Chao1 index: Originally developed by Dr Anne Chao to estimate richness in a ecological community

To create an amino V-genes plot

TCRcloud vgenes repertoire.airr.rearrangements.tsv

To export the processed data from the V-genes plot to a text file

TCRcloud vgenes repertoire.airr.rearrangements.tsv -e True

To create a 2-D amino acid plot

TCRcloud aminoacids repertoire.airr.rearrangements.tsv

To create a 3-D amino acid plot

TCRcloud aminoacids repertoire.airr.rearrangements.tsv -t True

To export the processed data from the amino acid plot to a text file

TCRcloud aminoacids repertoire.airr.rearrangements.tsv -e True

Using TCRcloud you can download rearragements files from the AIRR compliant databases based on AIRR repertoire metadata files

To download AIRR rearrangements files

TCRcloud download repertoire.airr.json

TCRcloud provides some test data to experiment the tool. The data is from subject number 8 of Yost et al. (2019) (DOI: 10.1038/s41591-019-0522-3)

To download the test data repertoire file

TCRcloud testdata

After having the alpharepertoire.airr.json and betarepertoire.airr.json file you can use the download function included in TCRcloud to get the matching rearragements file.