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Using Bulk Gene Expression to Estimate Cell-Type-Specific Gene Expression via Deconvolution

MIND 최신버젼 다운로드

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bMIND: Bayesian estimation of cell-type-specific (CTS) gene expression and CTS differential expression analysis
===============================================================

![](https://raw.githubusercontent.com/randel/MIND/master/man/figures/bMIND.png)

`bMIND` is a Bayesian deconvolution method to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we estimate cell-type-specific (CTS) expression from bulk tissue expression via MCMC.

## Installation

Installation requires the `devtools` package.

``` r
devtools::install_github('randel/MIND')
```
## Example

``` r
library(MIND)
data(example)
bulk = t(na.omit(apply(example$X, 1, as.vector)))
frac = na.omit(apply(example$W, 3, as.vector))
colnames(bulk) = rownames(frac) = 1:nrow(frac)
y = rbinom(n = nrow(frac), size = 1, prob = 0.5)
covariate = data.frame(c1 = rnorm(length(y)), c2 = rnorm(length(y)))

# CTS-DE (np = TRUE: use non-informative prior)
deconv = bmind_de(bulk, frac, y = y, covariate = covariate, covariate_bulk = 'c1', covariate_cts = 'c2', np = T)
 
# estimate CTS expression
deconv2 = bMIND(bulk, frac)
```

## Tutorials

**For detailes, please see the [tutorial](https://randel.github.io/MIND/).** It covers how to get prior distributions using multi-sample scRNA-seq data.

The cell type fraction can be pre-estimated using 1) non-negative least squares (NNLS), which requires a
signature matrix derived from reference samples of single-cell RNA-seq data; 2) Bisque, which requires raw single-cell data.

## Reference

**bMIND**: Wang, Jiebiao, Kathryn Roeder, and Bernie Devlin. "Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data." [_Genome Research_](https://genome.cshlp.org/content/31/10/1807.full.pdf) (2021) 31: 1807-1818.

MIND (frequentist method): see https://github.com/randel/MIND/blob/master/MIND.md

## Common errors

If you see strange error, first check the dimnames and each cell type should have an average fraction > 5\%.
The Bayesian software does not allow dots in the bulk sample name. Try
`colnames(bulk) = rownames(frac) = paste0('s', 1:nrow(frac))`

Also remember to remove any dot/space in the first cell type name, e.g.,
`colnames(frac) = paste0('c', 1:ncol(frac))`

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