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) . An overview of the field is presented in Caicedo (2016) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2.2 by Shantanu Singh, a year ago

Report a bug at

Browse source code at

Authors: Tim Becker [aut] , Allen Goodman [aut] , Claire McQuin [aut] , Mohammad Rohban [aut] , Shantanu Singh [aut, cre]

Documentation:   PDF Manual  

BSD_3_clause + file LICENSE license

Imports caret, doParallel, dplyr, foreach, futile.logger, magrittr, Matrix, purrr, rlang, tibble, tidyr

Suggests DBI, dbplyr, knitr, lazyeval, readr, rmarkdown, RSQLite, stringr, testthat

See at CRAN