Cell Ranger Output Filtering and Metrics Visualization.
CRMetrics
05-07-2023
Cell Ranger output filtering and metrics visualisation
Installation
install.packages("remotes")
remotes::install_github("khodosevichlab/CRMetrics") # CRAN version
remotes::install_github("khodosevichlab/CRMetrics", ref = "dev") # developer version
Initialization
A 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 fromcellranger count
. Can also beNULL
. Can also be a vector of multiple paths.cms
: A list with count matrices. Must be named with sample IDs. Can also beNULL
metadata
: Can either be 1) adata.frame
, or 2) a path to a table file (separator should be set with thesep.meta
argument), or 3)NULL
. For 1) and 2) the object must contain named columns, and one column has to be namedsample
containing sample IDs. Sample IDs must match the directory names indata.path
or names ofcms
unless both these areNULL
. In case of 3), a minimal metadata object is created from names indata.path
or names ofcms
.
Vignette
For usage, please see the vignette / code.
Python integrations
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.
Cite
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]