TESSA

Tessa is a Bayesian model to integrate T cell receptor (TCR) sequence profiling with transcriptomes of T cells.

Enabled by the recently developed single cell sequencing techniques, which provide both TCR sequences and RNA sequences of each T cell concurrently, Tessa maps the functional landscape of the TCR repertoire, and generates insights into understanding human immune response to diseases. As the first part of tessa, BriseisEncoder is employed prior to the Bayesian algorithm to capture the TCR sequence features and create numerical embeddings. We showed that the reconstructed Atchley Factor matrices and CDR3 sequences, generated through the numerical embeddings, are highly similar to their original counterparts. The CDR3 peptide sequences are constructed via a RandomForest model applied on the reconstructed Atchley Factor matrices.

See https://github.com/jcao89757/TESSA

When finished, two columns will be added to the meta.data of the Seurat object:

  • TESSA_Cluster: The cluster assignments from TESSA.
  • TESSA_Cluster_Size: The number of cells in each cluster.

These columns can be then used for further downstream analysis to explore the functional landscape of the TCR repertoire.

Note

The dependencies of TESSA are not included in the docker image of immunopipe with tag without -full suffix. If you want to use TESSA, please use the docker image with tag with -full suffix, or install the dependencies manually.

Input

  • screpdata: The data loaded by ScRepCombiningExpression, saved in RDS or qs/qs2 format.
    The data is actually generated by scRepertiore::combineExpression().
    The data must have both TRA and TRB chains.

Output

  • outfile: Default: {{in.screpdata | stem}}.tessa.qs.
    a qs fileof a Seurat object, with TESSA_Cluster and TESSA_Cluster_Size added to the meta.data

Environment Variables

  • python: Default: python.
    The path of python with TESSA's dependencies installed
  • within_sample (flag): Default: False.
    Whether the TCR networks are constructed only within TCRs from the same sample/patient (True) or with all the TCRs in the meta data matrix (False).
  • assay: Which assay to use to extract the expression matrix.
    Only works if in.srtobj is an RDS file of a Seurat object.
    By default, if SCTransform is performed, SCT will be used.
  • predefined_b (flag): Default: False.
    Whether use the predefined b or not.
    Please check the paper of tessa for more details about the b vector.
    If True, the tessa will not update b in the MCMC iterations.
  • max_iter (type=int): Default: 1000.
    The maximum number of iterations for MCMC.
  • save_tessa (flag): Default: False.
    Save tessa detailed results to seurat object?
    It will be saved to sobj@misc$tessa.

Reference

  • 'Mapping the Functional Landscape of TCR Repertoire.', Zhang, Z., Xiong, D., Wang, X. et al. 2021.
    link
  • 'Deep learning-based prediction of the T cell receptor-antigen binding specificity.', Lu, T., Zhang, Z., Zhu, J. et al. 2021.
    link

Metadata

The metadata of the Seurat object will be updated with the TESSA clusters and the cluster sizes:

TESSA-metadata