CRAN/E | cytominer

cytominer

Methods for Image-Based Cell Profiling

Installation

About

Typical morphological profiling datasets have millions of cells and hundreds of features per cell. When working with this data, you must clean the data, normalize the features to make them comparable across experiments, transform the features, select features based on their quality, and aggregate the single-cell data, if needed. 'cytominer' makes these steps fast and easy. Methods used in practice in the field are discussed in Caicedo (2017) doi:10.1038/nmeth.4397. An overview of the field is presented in Caicedo (2016) doi:10.1016/j.copbio.2016.04.003.

github.com/cytomining/cytominer
Bug report File report

Key Metrics

Version 0.2.2
R ≥ 3.3.0
Published 2020-05-09 1448 days ago
Needs compilation? no
License BSD_3_clause
License File
CRAN checks cytominer results

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Maintainer

Maintainer

Shantanu Singh

shsingh@broadinstitute.org

Authors

Tim Becker

aut

Allen Goodman

aut

Claire McQuin

aut

Mohammad Rohban

aut

Shantanu Singh

aut / cre

Material

README
NEWS
Reference manual
Package source

Vignettes

Introduction to cytominer

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

cytominer archive

Depends

R ≥ 3.3.0

Imports

caret ≥ 6.0.76
doParallel ≥ 1.0.10
dplyr ≥ 0.8.5
foreach ≥ 1.4.3
futile.logger ≥ 1.4.3
magrittr ≥1.5
Matrix ≥ 1.2
purrr ≥ 0.3.3
rlang ≥ 0.4.5
tibble ≥ 2.1.3
tidyr ≥ 1.0.2

Suggests

DBI ≥ 0.7
dbplyr ≥ 1.4.2
knitr ≥ 1.17
lazyeval ≥ 0.2.0
readr ≥ 1.1.1
rmarkdown ≥ 1.6
RSQLite ≥2.0
stringr ≥ 1.2.0
testthat ≥ 1.0.2