Package: scbs
scbs: A Command Line Tool for the Analysis of Single-Cell Bisulfite-Sequencing DataInstallationThis software requires a working installation ofPython 3and requires the use of a shell terminal.
It was extensively tested on Ubuntu Linux (18 and 20) and MacOS, and briefly tested on Windows 10.You can installscbsfrom the Python package index as follows:python3 -m pip install --upgrade pip  # you need a recent pip version
python3 -m pip install scbsInstallation ofscbsshould take no longer than a few seconds. All requireddependenciesare automatically installed, this may take a few minutes.
Afterwards, restart your terminal. The installation is now finished and the command line interface should now be available when typing the commandscbsin your terminal.
If this is not the case, check the "troubleshooting" section below.Updating to the latest versionJust use--upgradewhen installing the package, otherwise it's the same process as installing:python3 -m pip install --upgrade scbsAfterwards, make sure that the latest version is correctly installed:scbs --versionTutorialof a typicalscbsrunA tutorial can be foundhere. This gives instructions on how to usescbson a small example data set which we provide.Also make sure to read the help by typingscbs --helpor by checkingthis page.What can this package do?scbstakes as input a number of single-cell methylation files and allows you to quickly and easily obtain a cell × region matrix for downstream analysis (e.g. PCA, UMAP or clustering).
It also facilitates quality control, allows you to discover variably methylated regions (VMRs), accurately quantifies methylation in genomic intervals, and stores your sc-methylomes in an efficient manner.
Lastly, you can also select two cell populations and identify differentially methylated regions (DMRs) between them.You can find a list of the availablescbscommandshere.bioRxiv preprintFor a detailed explanation of the methods implemented inscbs, please check our bioRxiv preprint:Analyzing single-cell bisulfite sequencing data with scbsLukas PM Kremer, Leonie Kuechenhoff, Santiago Cerrizuela, Ana Martin-Villalba, Simon AndersbioRxiv 2022.06.15.496318; doi:https://doi.org/10.1101/2022.06.15.496318Hardware requirementsIt is recommended to use a computer with at least 16 gigabytes of RAM.
When analyzing small data sets (< 500 cells) like thetutorialdata set, 8 GB RAM should suffice.
Multiple CPU cores are not required but will greatly speed up some commands such asscbs scan.TroubleshootingInstallation issuesCarefully check the output log of PIP. Look for a message likeWARNING: The script scbs is installed in '/home/ubuntu/.local/bin' which is not on PATH., which would indicate that you need to add/home/ubuntu/.local/binto your path. Alternatively, you can copy/home/ubuntu/.local/bin/scbsto e.g./usr/local/bin.If you encounter other problems during installation, make sure you have Python3.8 or higher, and make sure you have the latest PIP version. If the problem persists, consider installingscbsin a clean Python environment (for example usingvenv).Too many open filesIf you encounter a "too many open files" error duringscbs prepare(OSError: [Errno 24] Too many open files), you need to increase the maximum number of files that can be opened. In Unix systems, tryulimit -n 9999.ContributorsLukas PM KremerMartina BraunLeonie KüchenhoffSvetlana OvchinnikovaAlexey UvarovskiiSimon Anders