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# Batch Effects Correction With Unknown Subtypes for scRNA-seq Data (BUSseq) ## Contents - [Overview](#overview) - [Repo Contents](#repo-contents) - [System Requirements](#system-requirements) - [Installation Guide](#installation-guide) - [Demo](#demo) - [Instructions for use](#instructions-for-use) - [License](./LICENSE) - [Citation](#citation) # Overview Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurement of the transcriptome of individual cells, which provides unprecedented opportunities to discover cell types and understand cellular heterogeneity. Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. One of the major tasks of scRNA-seq experiments is to identify cell types for a population of cells. Therefore, the cell type of each individual cell is always unknown and is the target of inference. However, most existing methods for batch effects correction, such as Combat and the surrogate variable analysis (SVA), are designed for bulk experiments and require knowledge of the subtype information, which corresponds to cell type information for scRNA-seq data, of each sample a priori. Here, the R package `BUSseq` fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our [manuscript]( # Repo Contents - [R](./R): `R` code. - [data](./data): the example data for the demo. - [inst/doc](./inst/doc): compiled user's guide and illustration of the applications of `BUSseq` package to the demo dataset. - [man](./man): help manual. - [src](./src): `C++` source code. - [tests](./tests): sample code for the demo dataset. - [vignettes](./vignettes): source code for the user's guide. # System Requirements ## Hardware Requirements The `BUSseq` package works on a standard personal computer (PC). The runtimes reported below were generated on an Ubuntu 18.04 operating system by a PC desktop with 8 GB RAM and 8 cores of 2.6 GHz. ## Software Requirements ### OS Requirements The package supports *Linux*, *Mac* and *Windows* operating systems. It has been tested on the following systems: Linux: Ubuntu 18.04 Mac OSX: Mac OS X 10.14 Mojave Windows: Windows 10 Enterprise ### Software dependencies Before installing the `BUSseq` package, users should have installed `R` with version 3.6.3 or higher. For Windows system, the users should also install [`Rtools`]( #### Package dependencies Users should install the following packages prior to installing `BUSseq`, from an `R` session: ``` install.packages(c('devtools', 'gplots', 'knitr')) ``` The installation will take about one minute. #### Package Versions The `BUSseq` package depends on the above packages with the following versions, respectively: ``` devtools: 2.2.2 gplots: 3.0.3 knitr: 1.28 ``` If you encounter any problem with installation, please drop us an [Issue]( # Installation Guide From an `R` session, type: ``` require(devtools) install_github("songfd2018/BUSseq-Rpackage") # install BUSseq ``` It takes approximately 30 seconds to install. # Demo Please check the user's guide for the detailed instructions on how to use the package by running the following code in the `R` session: ``` vignette("BUSseq_user_guide",package="BUSseq") # view the vignettes ``` For a given number of cell types *K*, it takes about 2 minutes to run 500 iterations in parallel on four cores. To select the optimal *K*, we need to compare the BIC values for different *K*s. In the vignettes, we enumerate *K* from 3 to 6. As a result, we need about 4 * 2 = 8 minutes. When we have a multi-core computer or a cluster, we can further run BUSseq with different *K*s in parallel. For the simulation study in our manuscript which consists of 3,000 genes and 1,000 cells, it takes about one hour to run 4,000 iterations on this dataset. # Citation Our work has been published on [Nature Communication]( If you use BUSseq for your work, please cite our paper. ``` @article{song2020flexible, title={Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction}, author={Song, Fangda and Chan, Ga Ming Angus and Wei, Yingying}, journal={Nature communications}, volume={11}, number={1}, pages={1--15}, year={2020}, publisher={Nature Publishing Group} } ```