CellDrift: inferring perturbation responses in temporally sampled single-cell data.
Kang JinDaniel SchnellGuangyuan LiNathan SalomonisV B Surya PrasathRhonda SzczesniakBruce J AronowPublished in: Briefings in bioinformatics (2022)
Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the 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 that is 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.
Keyphrases
- single cell
- data analysis
- gene expression
- rna seq
- induced apoptosis
- genome wide
- public health
- coronavirus disease
- copy number
- dna methylation
- cell therapy
- depressive symptoms
- high throughput
- risk assessment
- stem cells
- cell cycle arrest
- type diabetes
- electronic health record
- metabolic syndrome
- genome wide identification
- cell death
- cell proliferation
- transcription factor
- endoplasmic reticulum stress
- oxidative stress
- artificial intelligence