Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

Abstract

Motivation Single-cell sequencing now enables the recovery of full-length immune receptor repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate single-cell datasets incorporating immune receptor repertoires and gene expression. Results We developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, transcriptional phenotypes, and spatial location. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. We demonstrated the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Finally, we simulated immune repertoire information onto existing spatial transcriptomic experiments, thereby generating novel datasets that could be used to develop and integrate methods to profile clonal selection in a spatially-resolved manner. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

“…There exist various tools for simulating a set of individual AIRs with or without set parameters (Marcou, Mora, and Walczak 2018; C. R. Weber et al 2020; Woodcock, Bortone, and Vincent 2020; Safonova, Lapidus, and Lill 2015; Yermanos et al 2017; Han et al 2022; Sutherland and Cowan, n.d.; Yang et al 2021; Davidsen et al 2019; Pradier et al 2023; Isacchini et al 2021). These tools accurately mimic the V(D)J-recombination process to varying degrees, but most do not simulate the selection or implantation of putative immune signals (C. R. Weber et al 2020).…”

“…There exist various tools for simulating a set of individual AIRs with or without set parameters (39,(51)(52)(53)(54)(55)(56)(57)(58)(59)(60). These tools accurately mimic the V(D)J-recombination process to varying degrees, but most do not simulate the selection or implantation of putative immune signals (52).…”