Package index • TIRTLtools

Bulk TIRTLseq data

Data loading and wrangling

add_metadata(): Add missing metadata to a TIRTLseqData object.

filter_dataset(): Subset a TIRTLseqData object to only the requested samples

load_tirtlseq(): Load paired-TCR and pseudo-bulk data from TIRTLseq experiments into a TIRTLseqData object

reorder_samples(): Change the order of samples in a TIRTLseqData object

Data processing and cleaning

TIRTL_process(): Run the other data processing functions in a sequence (add_single_chain_data, clean_pairs, and identify_paired)

add_single_chain_data(): Add alpha- and beta-chain read counts and read fractions to the paired TCR data

clean_pairs(): Clean paired TCR data, keeping at most one beta-chain partner for each individual alpha-chain and at most two alpha-chain partners for each individual beta-chain

identify_non_functional_seqs(): Identify TCRs that contain non-functional CDR3 sequences, i.e. those that contain stop codons (*) or frame shifts (_)

identify_paired(): Identify which alpha and beta chains were able to be paired and which were not paired

prep_for_tcrdist(): Prepares paired TCRs for TCRdist calculation, adding alleles to V-segments (i.e. "*01") if they are missing and removing TCRs with unrecognized V-segments or non-functional CDR3 sequences

remove_duplicates(): Removes duplicate paired TCRs (by default, paired TCRs are listed twice if they were called by both pairing algorithms)

TCR repertoire analysis

TCRdist(): GPU implementation of TCRdist, a distance/similarity metric for pairs of TCRs

TCRdist_cpp(): Parallelized C++ implementation of TCRdist (no GPU required)

calculate_diversity() diversity(): Calculate TCR repertoire diversity metrics (Shannon diversity index, Simpson diversity index, etc.)

cluster_tcrs(): Cluster TCRs (using the Leiden algorithm) based on their pairwise TCRdist values

get_all_div_metrics(): Returns all diversity metric options for `calculate_diversity()`

get_all_tcrs(): Returns all of the paired TCRs from all samples in a dataset

get_pair_stats(): Count the number of alpha chains and beta chains that were paired by each pairing algorithm

get_paired_by_read_fraction_range(): Calculates the number and fraction of single chains that were paired within different read fraction ranges for each sample.

summarize_data(): Creates a summary table with number of reads and unique alpha/beta chains observed for each sample

Plotting

plot_clone_size_across_samples(): Line plot of clone read fraction across multiple samples

plot_clonotype_indices(): Stacked bar chart with fractions of reads attributed to the most frequent clonotypes

plot_clusters(): Plot clusters of similar TCRs

plot_diversity(): Bar plot of clonal diversity metrics

plot_n_reads(): Bar plot of the number of single-chain reads for each sample

plot_num_partners(): Stacked bar plot of the fraction of single-chains with different numbers of partners for each sample

plot_paired(): Stacked bar plot of the number alpha/beta chains paired by each pairing algorithm

plot_paired_by_read_fraction_range(): Bar plot of the fraction of single chains that were paired within different read fraction ranges for each sample.

plot_paired_vs_rank(): Line plot of the number of unpaired single-chains for the N most frequent single-chains

plot_pairs_with_eachother(): Point plot of read fraction vs. rank for the N most frequent alpha (left, mirrored) and beta (right) chains with lines between alpha and beta chains indicating a pair and a cross indicating an unpaired single-chain

plot_ranks() rank_plot(): Line plot of clonotype rank vs. read fraction for each sample

plot_read_fraction_vs_pair_status(): Alternative point plot of read fraction vs. rank for the N most frequent alpha and beta chains, with a cross indicating an unpaired single-chain

plot_sample_overlap() sample_overlap(): Plot the overlap/agreement between samples (in terms of most frequent clones)

plot_sample_vs_sample(): Scatterplot of TCR clone read fraction of clones between two samples

Export for external tools

create_thimble_df(): Convert paired TCRs to a "thimble" data frame for stitching together TCR nucleotide coding sequences with the Stitchr Python package

write_stitchr_tsv(): Write a tab-separated "thimble" text file for use with Stitchr

Well-level data functions

filter_well_data(): Remove rare clones from well-level TIRTLseq data

get_well_subset(): Get well names from numerical rows and columns

get_wells_from_edges(): Get well names from top left and bottom right wells

load_well_data(): Load well-level TIRTLseq data

Download datasets

download_dataset(): Download TIRTLseq datasets

Data

params: Table of permissible amino acids and V-segments for TCRdist

submat: Substitution penalty matrix for TCRdist amino acids and V-segments

Single-cell TIRTLseq data

clean_scTIRTLseq(): Imputes missing alpha and beta chains where possible for single-cell TIRTLseq data

summarize_scTIRTLseq(): Returns a summary of single-cell TIRTLseq data, printing the total number of wells with data, number of wells missing an alpha/beta chain, number of wells with a second alpha chain, etc.