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CRMetrics

Cell Ranger Output Filtering and Metrics Visualization

Sample and cell filtering as well as visualisation of output metrics from 'Cell Ranger' by Grace X.Y. Zheng et al. (2017) <doi:10.1038/ncomms14049>. 'CRMetrics' allows for easy plotting of output metrics across multiple samples as well as comparative plots including statistical assessments of these. 'CRMetrics' allows for easy removal of ambient RNA using 'SoupX' by Matthew D Young and Sam Behjati (2020) <doi:10.1093/gigascience/giaa151> or 'CellBender' by Stephen J Fleming et al. (2022) <doi:10.1101/791699>. Furthermore, it is possible to preprocess data using 'Pagoda2' by Nikolas Barkas et al. (2021) <https://github.com/kharchenkolab/pagoda2> or 'Seurat' by Yuhan Hao et al. (2021) <doi:10.1016/j.cell.2021.04.048> followed by embedding of cells using 'Conos' by Nikolas Barkas et al. (2019) <doi:10.1038/s41592-019-0466-z>. Finally, doublets can be detected using 'scrublet' by Samuel L. Wolock et al. (2019) <doi:10.1016/j.cels.2018.11.005> or 'DoubletDetection' by Gayoso et al. (2020) <doi:10.5281/zenodo.2678041>. In the end, cells are filtered based on user input for use in downstream applications.

README

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CRMetrics
================
05-07-2023

Cell Ranger output filtering and metrics visualisation

# Installation

``` r
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 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 `NULL`
-   `metadata`: 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`.

# Vignette

For usage, please see the
[vignette](http://kkh.bric.ku.dk/rasmus/CRMetrics/walkthrough.html)
/ [code](https://github.com/khodosevichlab/CRMetrics/blob/main/inst/docs/walkthrough.Rmd).

# Python integrations

CRMetrics makes use of several Python packages, some of them through the
`reticulate` package in R, please see the included [example
workflow](http://kkh.bric.ku.dk/rasmus/CRMetrics/walkthrough.html#using-python-modules)
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]

Versions across snapshots

VersionRepositoryFileSize
0.3.2 rolling linux/jammy R-4.5 CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 rolling linux/noble R-4.5 CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 rolling source/ R- CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 latest linux/jammy R-4.5 CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 latest linux/noble R-4.5 CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 latest source/ R- CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 2026-04-26 source/ R- CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 2026-04-23 source/ R- CRMetrics_0.3.2.tar.gz 224.7 KiB
0.3.2 2025-04-20 source/ R- CRMetrics_0.3.2.tar.gz 224.7 KiB

Dependencies (latest)

Imports

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