tpSVG

The goal of tpSVG is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, tpSVG introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to crmarkdown::pandoc_version()urrently available count-based model for spatially resolved data analysis, the tpSVG models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.

Installation

GitHub

You can install the development version of tpSVG from GitHub with:

#' Install devtools package if not already installed
if (!required(devtools)) install.packages(package_name)
devtools::install_github("boyiguo1/tpSVG")

If you have R version before v4.4 and would like to install tpSVG, you can follow

if (!require("devtools")) install.packages("devtools")
devtools::install_github("boyiguo1/tpSVG@pre-R4.4")

WARNING: The purpose of having the branch pre-R4.4 is to allow users to use escheR before the formal release of R 4.4 and during the early stage of R 4.4 release. This branch will not be update with any further development beyond escheR v0.99.1. We recommend users to update their R versions up to date.

Bioconductor (pending)

The package is currently submitted to Bioconductor for review. Once the package is accepted by Bioconductor, you can install the latest release version of tpSVG from Bioconductor via the following code. Additional details are shown on the Bioconductor page.

# NOTE: The package is under-review with bioconductor.
#       The following code section will work once the package is accepted.

if (!require("BiocManager", quietly = TRUE)) {
  install.packages("BiocManager")
}
BiocManager::install("tpSVG")

The latest development version can also be installed from the devel version of Bioconductor or from GitHub following

BiocManager::install(version = "devel")

Tutorial

Please find an end-to-end tutorial at https://boyi-guo.com/tpSVG/articles/intro_to_tpSVG.html.

Frequently asked questions

Implementation Questions

• What are the data structures that tpSVG current supports?

  As of tpSVG v0.99.1, the data structure tpSVG supports includes SpatialExperiments (and packages extending SpatialExperiments, e.g. SpatialFeatureExperiments) and data.frame. Please find example via supported_data_structure. Due to limited resources, we regret that we won’t provides direct accessibility to other pipelines, e.g. suerat.

• What types of spatially-resolved transcriptomics (SRT) data that tpSVG supports?

  Both sequenced-based SRT and image-based SRT data are supported by tpSVG. For more details, please refer to the vignette supported_data_structure.

• Can I use other scale factor as offset in the count-model?

  Yes, just remember to take log for the offset term. In the vignettes, the offset of the model is default to library size, i.e. the total number of molecular in a spot/cell, but the count models should be compatible to other definition of scale factor in theory.

Theoretical Questions

• What is the difference between modeling log transformed data and count data?

  Count data is the natural form of gene expression data when it is collected and quantified. While log-transformation providess shortcuts to model (normalized) count data using well-studied Gaussian distribution, it distorts the lowly expressed gene and causes analytic biases.