<!-- badges: start --> [](https://bioconductor.org/checkResults/release/bioc-LATEST/APL) [](https://bioconductor.org/checkResults/devel/bioc-LATEST/APL) [](http://bioconductor.org/packages/stats/bioc/APL/) [](https://support.bioconductor.org/tag/APL) [](https://bioconductor.org/packages/release/bioc/html/APL.html#since) [](http://bioconductor.org/checkResults/devel/bioc-LATEST/APL/) [](https://bioconductor.org/packages/release/bioc/html/APL.html#since) <!-- badges: end --> <img src="man/figures/fig_AP.png" width="700"> # APL `APL` is a package developed for computation of Association Plots, a method for visualization and analysis of single cell transcriptomics data. The main focus of `APL` is the identification of genes characteristic for individual clusters of cells from input data. When working with `APL` package please cite: ``` Gralinska, E., Kohl, C., Fadakar, B. S., & Vingron, M. (2022). Visualizing Cluster-specific Genes from Single-cell Transcriptomics Data Using Association Plots. Journal of Molecular Biology, 434(11), 167525. ``` ## Installation The `APL` can be installed from GitHub: library(devtools) install_github("VingronLab/APL") To additionally build the package vignette, run instead: install_github("VingronLab/APL", build_vignettes = TRUE, dependencies = TRUE) Building the vignette will however take considerable time. **The vignette can also be found under the link: https://vingronlab.github.io/APL/ (hyperlink in the GitHub repository description).** To install the `APL` from Bioconductor, run: if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("APL") ## Pytorch installation In order to speed up the singular value decomposition, we highly recommend the installation of `pytorch`. Users can instead also opt to use the slower R native SVD. For this, please set the argument `python = FALSE` wherever applicable in the package vignette. ### Install pytorch with reticulate library(reticulate) install_miniconda() conda_install(envname = "r-reticulate", packages = "numpy") conda_install(envname = "r-reticulate", packages = "pytorch") ### Manually install pytorch with conda Download the appropriate Miniconda installer for your system from [the conda website](https://docs.conda.io/en/latest/miniconda.html). Follow the installation instructions on their website and make sure the R package `reticulate` is also installed before proceeding. Once installed, list all available conda environments via <br> `conda info --envs` <br> One of the environments should have `r-reticulate` in its name. Depending on where you installed it and your system, the exact path might be different. Activate the environment and install pytorch into it. conda activate ~/.local/share/r-miniconda/envs/r-reticulate # change path accordingly. conda install numpy conda install pytorch ## Feature overview Please run vignette("APL") after installation with `build_vignettes = TRUE` for an introduction into the package.