05-07-2023
Cell Ranger output filtering and metrics visualisation
install.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.
CRMetrics 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]