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: |
0.2.2 |
| 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) |
| Published: |
2020-05-09 |
| Author: |
Tim Becker [aut],
Allen Goodman [aut],
Claire McQuin [aut],
Mohammad Rohban [aut],
Shantanu Singh [aut, cre] |
| Maintainer: |
Shantanu Singh <shsingh at broadinstitute.org> |
| BugReports: |
https://github.com/cytomining/cytominer/issues |
| License: |
BSD_3_clause + file LICENSE |
| URL: |
https://github.com/cytomining/cytominer |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
cytominer results |