CellDrift: Inferring Perturbation Responses in Temporally-Sampled Single Cell Data

Abstract

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories, and outcomes.

scPerturb: Harmonized Single-Cell Perturbation Data

“…Such integrative analysis is complicated by batch effects and biological differences between primary tissue and cell culture (Forcato et al, 2021; Luecken et al, 2022). Published computational methods for perturbation data are primarily focused on individual datasets (Duan et al, 2019; Jin et al, 2022; Lotfollahi et al, 2019). Moving from single-dataset to multi-dataset analysis will require development of principled quantitative approaches to perturbation biology; the dataset resource based on this work can serve as a foundation for building these models going forward.…”