cytominer: Methods for Image-Based Cell Profiling

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>.

Version: 0.1.0
Depends: R (≥ 3.3.3)
Imports: caret (≥ 6.0.76), doParallel (≥ 1.0.10), dplyr (≥ 0.7.2), foreach (≥ 1.4.3), futile.logger (≥ 1.4.3), magrittr (≥ 1.5), purrr (≥ 0.2.3), rlang (≥ 0.1.2), tibble (≥ 1.3.4), tidyr (≥ 0.7.1)
Suggests: DBI (≥ 0.7), dbplyr (≥ 1.1.0), 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)
Published: 2017-09-17
Author: Tim Becker [aut], Allen Goodman [aut], Claire McQuin [aut], Mohammad Rohban [aut], Shantanu Singh [aut, cre]
Maintainer: Shantanu Singh <shsingh at>
License: BSD_3_clause + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: cytominer results


Reference manual: cytominer.pdf
Vignettes: Introduction to cytominer
Package source: cytominer_0.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: cytominer_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: not available


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