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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# 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_github, eval=FALSE}
#' 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/[email protected]")
```
> 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 install_bioc, eval = FALSE}
# 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 install_bioc_dev, eval = FALSE}
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._