# tpSVG \[!\[Lifecycle: experimental\](https://img.shields.io/badge/lifecycle-experimental-orange.svg)\](https://lifecycle.r-lib.org/articles/stages.html#experimental) \[!\[R build status\](https://github.com/boyiguo1/tpSVG/workflows/R-CMD-check-bioc/badge.svg)\](https://github.com/boyiguo1/tpSVG/actions) 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 . ## 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\\\]\\\]().\* - 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.\*
\[!\[Lifecycle: experimental\](https://img.shields.io/badge/lifecycle-experimental-orange.svg)\](https://lifecycle.r-lib.org/articles/stages.html#experimental) \[!\[R build status\](https://github.com/boyiguo1/tpSVG/workflows/R-CMD-check-bioc/badge.svg)\](https://github.com/boyiguo1/tpSVG/actions) 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 . ## 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\\\]\\\]().\* - 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.\*