<!-- README.md is generated from README.Rmd. Please edit that file --> # tpSVG <img src="logo.png" align="right" height="139" alt="" /> <!-- badges: start --> [](https://lifecycle.r-lib.org/articles/stages.html#experimental) [](https://github.com/boyiguo1/tpSVG/actions) <!-- badges: end --> 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](https://github.com/boyiguo1/tpSVG) with: ``` r #' 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](https://github.com/Bioconductor/Contributions/issues/3264). 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. ``` r # 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 ``` r 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`](https://bioconductor.org/packages/release/bioc/html/SpatialFeatureExperiment.html) (and packages extending `SpatialExperiments`, e.g. [`SpatialFeatureExperiments`](https://bioconductor.org/packages/release/bioc/html/SpatialFeatureExperiment.html)) and `data.frame`. Please find example via [supported_data_structure](https://boyi-guo.com/escheR/articles/supported_data_structure.html). 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 \[supported_data_structure\]\](<https://boyi-guo.com/tpSVG/articles/supported_data_structure.html#image-based-srt-in-spatialexperiment-e-g--spatialfeatureexperiment>).* - 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.*