Bulk and single-cell data

Loading and saving any data

repLoad()

Load immune repertoire files into the R workspace

repSave()

Save immune repertoires to the disk

Filtering data

repFilter()

Main function for data filtering

Single-cell

More coming soon!

select_barcodes()

Select specific clonotypes using barcodes from single-cell metadata

select_clusters()

Split the immune repertoire data to clusters from single-cell barcodes

Preprocessing

bunch_translate()

Nucleotide to amino acid sequence translation

coding() noncoding() inframes() outofframes()

Filter out coding and non-coding clonotype sequences

repSample()

Downsampling and resampling of immune repertoires

top()

Get the N most abundant clonotypes

Basic immune repertoire statistics

Exploratory data analysis

repExplore()

Main function for exploratory data analysis: compute the distribution of lengths, clones, etc.

vis(<immunr_exp_vol>)

Visualise results of the exploratory analysis

Clonality analysis

repClonality()

Clonality analysis of immune repertoires

vis(<immunr_clonal_prop>)

Visualise results of the clonality analysis

Clonotype annotation and dynamics

Clonotype annotation

Annotate clonotypes in immune repertoires using external immune receptor databases (VDJDB, McPAS and PIRD).

dbAnnotate()

Annotate clonotypes in immune repertoires using clonotype databases such as VDJDB and MCPAS

dbLoad()

Load clonotype databases such as VDJDB and McPAS into the R workspace

Immune repertoire dynamics

Track the differences in clonotype abundances over time.

trackClonotypes()

Track clonotypes across time and data points

vis(<immunr_dynamics>)

Visualise clonotype dynamics

Compare repertoires

Repertoire diversity analysis

repDiversity()

Main function for immune repertoire diversity estimation

vis(<immunr_chao1>)

Visualise diversity.

Gene usage

V-gene and J-gene usage statistics, analysis and visualisations

geneUsage()

Main function for estimation of V-gene and J-gene statistics

geneUsageAnalysis()

Post-analysis of V-gene and J-gene statistics: PCA, clustering, etc.

gene_stats()

WIP

vis(<immunr_gene_usage>)

Histograms and boxplots (general case / gene usage)

Overlap

repOverlap()

Main function for public clonotype statistics calculations

repOverlapAnalysis()

Post-analysis of public clonotype statistics: PCA, clustering, etc.

inc_overlap()

Incremental counting of repertoire similarity

vis(<immunr_inc_overlap>)

Visualise incremental overlaps

vis(<immunr_ov_matrix>)

Repertoire overlap and gene usage visualisations

Public repertoire

pubRep()

Create a repertoire of public clonotypes

pubRepApply()

Apply transformations to public repertoires

pubRepFilter()

Filter out clonotypes from public repertoires

pubRepStatistics()

Statistics of number of public clonotypes for each possible combinations of repertoires

public_matrix()

Get a matrix with public clonotype frequencies

vis(<immunr_public_repertoire>)

Public repertoire visualisation

vis(<immunr_public_statistics>)

Visualise sharing of clonotypes among samples

vis_public_clonotypes()

Visualisation of public clonotypes

vis_public_frequencies()

Public repertoire visualisation

Advanced immune repertoire analysis

Kmers analysis

getKmers()

Calculate the kmer statistics of immune repertoires

split_to_kmers() kmer_profile()

Analysis immune repertoire kmer statistics: sequence profiles, etc.

vis(<immunr_kmer_table>)

Most frequent kmers visualisation.

vis_immunr_kmer_profile_main()

Visualise kmer profiles

vis_textlogo() vis_seqlogo()

Sequence logo plots for amino acid profiles.

Post-analysis

Advanced methods for post-analysis of gene usage, overlap and other statistics.

immunr_hclust() immunr_kmeans() immunr_dbscan()

Clustering of objects or distance matrices

immunr_pca() immunr_mds() immunr_tsne()

Dimensionality reduction

vis(<immunr_hclust>)

Visualisation of hierarchical clustering

vis(<immunr_kmeans>)

Visualisation of K-means and DBSCAN clustering

vis(<immunr_mds>)

PCA / MDS / tSNE visualisation (mainly overlap / gene usage)

Visualisations

General visualisation functions

Functions for visualisations of different data types. For analysis-specific visualisation see related sections.

vis()

One function to visualise them all

vis(<immunr_chao1>)

Visualise diversity.

vis(<immunr_clonal_prop>)

Visualise results of the clonality analysis

vis(<immunr_dynamics>)

Visualise clonotype dynamics

vis(<immunr_exp_vol>)

Visualise results of the exploratory analysis

vis(<immunr_gene_usage>)

Histograms and boxplots (general case / gene usage)

vis(<immunr_hclust>)

Visualisation of hierarchical clustering

vis(<immunr_inc_overlap>)

Visualise incremental overlaps

vis(<immunr_kmeans>)

Visualisation of K-means and DBSCAN clustering

vis(<immunr_kmer_table>)

Most frequent kmers visualisation.

vis(<immunr_mds>)

PCA / MDS / tSNE visualisation (mainly overlap / gene usage)

vis(<immunr_ov_matrix>)

Repertoire overlap and gene usage visualisations

vis(<immunr_public_repertoire>)

Public repertoire visualisation

vis(<immunr_public_statistics>)

Visualise sharing of clonotypes among samples

vis_bar()

Bar plots

vis_box()

Flexible box-plots for visualisation of distributions

vis_circos()

Visualisation of matrices using circos plots

vis_heatmap()

Visualisation of matrices and data frames using ggplo2-based heatmaps

vis_heatmap2()

Visualisation of matrices using pheatmap-based heatmaps

vis_hist()

Visualisation of distributions using histograms

vis_immunr_kmer_profile_main()

Visualise kmer profiles

vis_public_clonotypes()

Visualisation of public clonotypes

vis_public_frequencies()

Public repertoire visualisation

vis_textlogo() vis_seqlogo()

Sequence logo plots for amino acid profiles.

spectratype()

Immune repertoire spectratyping

Publication-ready plots

fixVis()

Manipulate ggplot plots and make publication-ready plots

Utilities

Data

aa_properties

Tables with amino acid properties

AA_TABLE

Amino acid / codon table

gene_segments

Gene segments table

immdata

Single chain immune repertoire dataset

immunr_data_format

Specification of the data format used by immunarch dataframes

scdata

Paired chain immune repertoire dataset

Public utility utilities

apply_symm() apply_asymm()

Apply function to each pair of data frames from a list.

entropy() kl_div() js_div() cross_entropy()

Information measures

Internal utility functions

add_class()

Add a new class attribute

check_distribution()

Check and normalise distributions

.quant_column_choice()

Get a column's name using the input alias

group_from_metadata()

Get a character vector of samples' groups from the input metadata file

has_class()

Check for the specific class

matrixdiagcopy()

Copy the upper matrix triangle to the lower one

set_pb() add_pb()

Set and update progress bars

switch_type() process_col_argument()

Return a column's name