05-07-2023
Cell Ranger output filtering and metrics visualisation
Installationinstall.packages("remotes")
remotes::install_github("khodosevichlab/CRMetrics") # CRAN version
remotes::install_github("khodosevichlab/CRMetrics", ref = "dev") # developer versionA CRMetrics object can be initialized in different ways using CRMetrics$new(). Either data.path or cms must be provided. The most important arguments are:
data.path: A path to a directory containing sample-wise directories with outputs from cellranger count. Can also be NULL. Can also be a vector of multiple paths.cms: A list with count matrices. Must be named with sample IDs. Can also be NULLmetadata: Can either be 1) a data.frame, or 2) a path to a table file (separator should be set with the sep.meta argument), or 3) NULL. For 1) and 2) the object must contain named columns, and one column has to be named sample containing sample IDs. Sample IDs must match the directory names in data.path or names of cms unless both these are NULL. In case of 3), a minimal metadata object is created from names in data.path or names of cms.For usage, please see the vignette / code.
Python integrationsCRMetrics makes use of several Python packages, some of them through the reticulate package in R, please see the included example workflow in the vignette.
To cite this work, please run citation("CRMetrics") or cite our preprint:
Fabienne Lorena Kick, Henrietta Holze, Rasmus Rydbirk, Konstantin Khodosevich: CRMetrics - an R package for Cell Ranger Filtering and Metrics Visualisation, 06 July 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2853524/v1]
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4