BOOST-GP

Bayesian mOdeling Of Spatial Transcriptomics via Gaussian Process

Introduction

BOOST-GP is a Bayesian hierarchical model developed to analyze spatial transcriptomics data. It models the gene expression count value with zero-inflated negative binomial distribution, and estimated the spatial covariance with Gaussian process model. It can be applied to detect spatial variable (SV) genes whose expression display spatial pattern.

BOOST-GP was developed and tested under R 3.6.1. The following R packages are required to run the model

• Rcpp
• RcppArmadillo
• RcppDist
• MASS

Run BOOST-GP on demo data

The following section will guide to run a exemplary data using BOOST-GP.

Load BOOST-GP

source("R/boost.gp.R")

Load demo data

BOOST-GP requires two inputs:

1. a count matrix with each row representing one spatial location, and each column representing one gene.

2. a location matrix specifying the x and y coordinates of each location.

load("data/demo.Rdata")
head(count_matrix)

	ARL6IP4	RPL35	FAM173A	SNRNP70	RALY
17.907x4.967	0	1	0	0	0
18.965x5.003	0	1	0	0	4
18.954x5.995	0	1	0	0	0
17.846x5.993	0	2	0	0	0
20.016x6.019	0	3	0	0	0
20.889x6.956	0	2	0	1	0

head(loc)

	x	y
17.907x4.967	17.907	4.967
18.965x5.003	18.965	5.003
18.954x5.995	18.954	5.995
17.846x5.993	17.846	5.993
20.016x6.019	20.016	6.019
20.889x6.956	20.889	6.956

Run the model

We run boost.gp with its defaulting setting on the above demo data.

res <- boost.gp(Y = count_matrix, loc = loc)

[1] "Chain 1"
0% has been done
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[1] "runtime is 146.703s"

Following is the output of boost.gp. l is the estimated length scale of Gaussian process kernel. BF is the Bayes factor. PPI is the posterior probablity of inclusion. The larger BF and PPI are, the stronger the confidence of the gene being spatial variable gene is. pval is the p value. Small pval indicates strong confidence.

res

	l	BF	PPI	pval	time
ARL6IP4	0.0818	1.058	0.010	1.456e-01	29.34
RPL35	0.6068	17.711	0.904	2.652e-09	29.34
FAM173A	0.0989	0.459	0.008	3.376e-01	29.34
SNRNP70	0.0876	0.928	0.022	1.730e-01	29.34
RALY	0.0000	0.379	0.000	3.839e-01	29.34

Visualize the significant gene

Based on the above result, RPL35 is a significant spatial variable gene, we plot its log transformed expression value to have a visual examination.

plot.expr(log(count_matrix[,"RPL35"] + 1), loc)