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FAST is an advanced probabilistic factor analysis technique designed for spatially-aware dimension reduction in multi-section spatial transcriptomics data. The ‘ProFAST’ package incorporates the FAST method and is specifically developed by the Jin Liu’s lab for the comprehensive analysis of multiple spatially resolved transcriptomics (SRT) datasets. Recently, we developed the coembedding method CoFAST that simultaneously estimates the cells/spots and features embeddings in the same space. This coembedding space benefits the signature gene identification and visualization.
Check out our Package Website for a more complete description of the methods and analyses.
FAST can be used to compare and contrast experimental datasets in a variety of contexts, for instance:
- Across experimental batches
- Across individuals
- Across different conditions (i.e., case and control)
- Across datasets with only partially shared cell/domain clusters
Once the embeddings of multiple datasets are estimated by FAST, the package provides functionality for further data exploration, analysis, and visualization. Users can:
- Align the embeddings obtained by ProFAST
- Identify clusters using all data information
- Recover comparable gene expression matrices among datasets
- Find significant shared (and dataset-specific) gene markers
- Visuzlize extracted embeddings using 3-dim tSNE and UMAP
- Visualize clusters and gene expression using tSNE and UMAP
Once the coembeddings of dataset are estimated by CoFAST, the package provides functionality for further data exploration, analysis, and visualization. Users can:
- Find the signature genes
- Visuzlize the coembeddings on UMAP space
- Visuzlize the signature genes on UMAP space
Installation
“ProFAST” depends on the ‘Rcpp’ and ‘RcppArmadillo’ package, which requires appropriate setup of computer. For the users that have set up system properly for compiling C++ files, the following installation command will work.
# Method 1: install ProFAST from CRAN
install.packages('ProFAST')
# For the newest version of ProFAST, users can use method 2 for installation.
# Method 2: Install ProFAST from Github
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("feiyoung/ProFAST")
# If some dependent packages (such as `scater`) on Bioconductor can not be installed nomrally, use following commands, then run abouve command.
if (!require("BiocManager", quietly = TRUE)) ## install BiocManager
install.packages("BiocManager")
# install the package on Bioconducter
BiocManager::install(c("scater"))Usage
For usage examples and guided walkthroughs, check the vignettes directory of the repo.
Tutorials for FAST method:
- Single-section analysis for DLPFC data
- Integration analysis for a toy example with three batches
- Integration analysis for DLPFC data with two batches
Tutorials for CoFAST method:
For the users that don’t have set up system properly, the following setup on different systems can be referred. ## Setup on Windows system First, download Rtools; second, add the Rtools directory to the environment variable.
Setup on MacOS system
First, install Xcode. Installation about Xcode can be referred here.
Second, install “gfortran” for compiling C++ and Fortran at here.
Setup on Linux system
If you use conda environment on Linux system and some dependent packages (such as scater) can not normally installed, you can search R package at anaconda.org website. We take the scater package as example, and its search result is https://anaconda.org/bioconda/bioconductor-scater. Then you can install it in conda environment by following command.
conda install -c bioconda bioconductor-scaterFor the user not using conda environment, if dependent packages (such as scater) not normally installed are in Bioconductor, then use the following command to install the dependent packages.
# install BiocManager
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# install the package on Bioconducter
BiocManager::install(c("scater"))If dependent packages (such as DR.SC) not normally installed are in CRAN, then use the following command to install the dependent packages.
# install the package on CRAN
install.packages("DR.SC")Demonstration
For an example of typical ProFAST usage, please see our Package Website for a demonstration and overview of the functions included in ProFAST.