general0710 / mind

Using Multiple Measurements of Tissue to Estimate Subject- And Cell-Type-Specific Gene Expression via Deconvolution

MIND (Multi-measure INdividual Deconvolution)

Using Multiple Measurements of Tissue to Estimate Subject- And Cell-Type-Specific Gene Expression

MIND is a method to glean insights from bulk gene expression. It borrows information across multiple measurements of the same tissue per subject, such as multiple regions of the brain, using an empirical Bayes approach to estimate subject- and cell-type-specific gene expression via deconvolution.

Installation

Installation requires the devtools package.

devtools::install_github('randel/MIND')

Example

Estimate subject- and cell-type-specific gene expression (saved as deconv$A below) using bulk gene expression data and pre-estimated cell type fractions:

• X: bulk gene expression (gene x subject x measure)
• W: cell type fraction (subject x measure x cell type)

library(MIND)

data(example)

deconv = mind(X = example$X, W = example$W)

For details, please see the PDF manual.

The cell type fraction can be pre-estimated using est_frac() (based on non-negative least squares, see an example here) or another standard deconvolution method. It requires a signature matrix derived from reference samples of single-cell RNA-seq data.

Reference

Jiebiao Wang, Bernie Devlin, Kathryn Roeder. Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression. Bioinformatics, https://doi.org/10.1093/bioinformatics/btz619