STADIA

CRAN Version

Overview

STADIA is designed to integrate multiple spatial transcriptomics slices, which simultaneously achieve

  • dimension reduction
  • batch effects correction
  • spatial domains identification

Overall, STADIA is a Bayesian hierarchical hidden Markov random field model that first uses Bayesian factor regression and location-and-scale adjustment to learn a batch-corrected low-dimensional representation of the gene expression profiles, and then spatially clusters the embedding using a Gaussian mixture model with a Potts spatial prior to promote local consistency.

The homepage for the R package stadia can be accessed at: https://yanfang-li.github.io/STADIA/index.html.

Installation

It’s recommended to create a separate conda environment for running STADIA:

conda create -n env_stadia -c conda-forge r-base r-essentials
conda activate env_stadia

It is a good practice to install Bioconductor dependencies before installing package stadia.

# Install Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager") 
BiocManager::install(c("BiocSingular", "BiocParallel", "BiocNeighbors"))

The stadia’s dependency package mombf depends on sparseMatrixStats, which sometimes cannot be installed automatically, so we install sparseMatrixStats first to prevent errors later:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("sparseMatrixStats")

Install the stadia package from GitHub (may take some time):

# Install released version from github
if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_github("yanfang-li/STADIA")

or from the source in the terminal ( Package source):

R CMD INSTALL stadia_1.0.1.tar.gz

or from precompiled binary package (Windows binaries):

The stadia package has been built and tested on the following operating system:

  • Windows: 10, version 22H2 [Rtools required]
  • Linux: Ubuntu 20.04.4 LTS
  • MacOS: Monterey 12.7.1 (Apple M1), Monterey 12.0.1 (Intel Core i7)

Setup to install source package stadia on MacOS.

Since the stadia package uses C++ and openmp, some additional configuration is required to install it from source on MacOS. For details, please refer to the following steps:

  1. Install Command Line Tools for Xcode from https://developer.apple.com/download/all.
  2. Download homebrew from https://github.com/Homebrew/brew/releases/tag/4.1.24 and install it.
  3. Install gcc using brew install gcc.
  4. Install LLVM using brew install llvm.
  5. Install libomp package using brew install libomp.
  6. Make file ~/.R/Makevars: (all paths must to be replaced with the paths on your own computer)
# MacOS Xcode header location
# (do "xcrun -show-sdk-path" in terminal to get path)
XC_LOC = /Applications/Xcode.13.4.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX.sdk
# (do "echo $(brew --prefix **)" in terminal to get path)
LLVM_LOC = /opt/homebrew/opt/llvm
GCC_LOC = /opt/homebrew/Cellar/gcc/12.2.0
GETTEXT_LOC = /opt/homebrew/opt/gettext
OMP_LOC= /opt/homebrew/opt/libomp
OPENSSL_LOC=/opt/homebrew/opt/openssl@1.1

CC=$(LLVM_LOC)/bin/clang
CXX=$(LLVM_LOC)/bin/clang++
CXX11=$(LLVM_LOC)/bin/clang++
CXX14=$(LLVM_LOC)/bin/clang++
CXX17=$(LLVM_LOC)/bin/clang++
CXX1X=$(LLVM_LOC)/bin/clang++

CFLAGS=-g -O2 -Wall -pedantic -std=gnu99 -mtune=native -pipe
CXXFLAGS=-g -O2 -Wall -pedantic -std=c++11 -mtune=native -pipe
LDFLAGS=-L"$(LLVM_LOC)/lib" -L"$(GETTEXT_LOC)/lib" -Wl,-rpath,$(LLVM_LOC)/lib --sysroot="$(XC_LOC)" -lomp -L"$(OPENSSL_LOC)/lib"
CPPFLAGS=-I"$(GETTEXT_LOC)/include" -I"$(LLVM_LOC)/include" -I"$(OMP_LOC)/include" -isysroot "$(XC_LOC)" -Xclang -fopenmp -I"$(OPENSSL_LOC)/include"

FC=$(GCC_LOC)/bin/gfortran
F77=$(GCC_LOC)/bin/gfortran
FLIBS=-L$(GCC_LOC)/lib/gcc/11/ -lm

Steps to use

The main steps to run the STADIA algorithm using the stadia package are (it is recommended to run in the server background for large data sets)

## load packages
library(stadia)
## set hyperparameters
hyper <- HyperParameters(obj, dim = d, eta = eta)
## run model
out <- stadia(obj, hyper, dim = d, n_cluster = K, 
             platform = "visium")

where

  • obj: a list of Seurat objects containing information of ST slices
  • dim: the dimension of latent factors
  • eta: the spatial smoothness parameter used in Potts model
  • n_cluster: the number of spatial domains
  • platform: specifics the platform

Demonstration

Files in the Applications demonstrate how to use the stadia package to run the STADIA algorithm in the Run section.

Paper Citation

Yanfang Li and Shihua Zhang (2024). “Statistical batch-aware embedded integration, dimension reduction and alignment for spatial transcriptomics”, Bioinformatics, btae611, https://doi.org/10.1093/bioinformatics/btae611.