SPARKLE is based on generalized linear mixed models (GLMM) for large-scale single-cell cell-phenotype association analysis. SPARKLE supports the flexibly incorporation of metadata variables as covariates to mitigate the impact of heterogeneity on result accuracy.
Running the package requires a working R environment (>=3.5).
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("gao-lab/SPARKLE", dep=TRUE, force=TRUE)
Warning: For Linux or MacOS users, if cmake is not installed, there might be errors during the installation of lme4 and bruceR dependencies. Please install the following first:
conda install -c anaconda cmake
conda install -c conda-forge r-lme4
conda install -c bioconda r-brucer
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SPARKLE is a model-based toolkit for calling Cell Type–Phenotype Associations across massive heterogenous datasets.
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By performing precise statistical tests to detect different types of association, SPARKLE enables identification of key cell types associated with particular phenotype(s) with effect sizes (odds ratios, OR) estimated.
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SPARKLE supports confounding analysis, mediation effect analysis, and masking effect analysis for tool cells, explaining how tool cells influence the association between target cells and phenotypes.
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SPARKLE further allows moderation effect analysis between cells and generates network diagrams.
SPARKLE provides five real-world cases for users:
Tutorial 01: Supported Data Input Formats
Tutorial 02: Example Case: COVID19 dataset
Tutorial 03: Example Case: MNP dataset
Tutorial 04: Example Case: Tcell dataset
Tutorial 05: Example Case: Fibroblast dataset
A cross-tissue, multidisease fibrotic disease fibroblast atlas (FDFA) is publicly available at https://zenodo.org/records/14011564
Xi Chen, Yang Ding, Shiqi Yang, Lin Wei, Ming Chu, Hao Tang, Lei Kong, Yixin Zhou, Ge Gao. Identifying Key Cells for Fibrosis by Systematically Calling Cell Type–Phenotype Associations across Massive Heterogenous Datasets. (in submission)
Email: [email protected] <<<<<<< HEAD
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