Learn R Programming

cytominer

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 so that they are comparable across experiments

  • transform the features so that their distributions are well-behaved ( i.e., bring them in line with assumptions we want to make about their disributions)

  • select features based on their quality

  • aggregate the single-cell data, if needed

The cytominer package makes these steps fast and easy.

Installation

You can install cytominer from CRAN:

install.packages("cytominer")

Or, install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("cytomining/cytominer", dependencies = TRUE, build_vignettes = TRUE)

Occasionally, the Suggests dependencies may not get installed, depending on your system, so you'd need to install those explicitly.

Example

See vignette("cytominer-pipeline") for basic example of using cytominer to analyze a morphological profiling dataset.

Copy Link

Version

Install

install.packages('cytominer')

Monthly Downloads

239

Version

0.2.2

License

BSD_3_clause + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Shantanu Singh

Last Published

May 9th, 2020

Functions in cytominer (0.2.2)

generate_component_matrix

A sparse matrix for sparse random projection.
extract_subpopulations

Extract subpopulations.
svd_entropy

Feature importance based on data entropy.
generalized_log

Generalized log transform data.
transform

Transform observation variables.
sparse_random_projection

Reduce the dimensionality of a population using sparse random projection.
replicate_correlation

Measure replicate correlation of variables.
normalize

Normalize observation variables.
aggregate

Aggregate data based on given grouping.
correlation_threshold

Remove redundant variables.
variable_select

Select observation variables.
variable_importance

Measure variable importance.
covariance

Compute covariance matrix and vectorize.
count_na_rows

Count the number of NAs per variable.
drop_na_columns

Remove variables with NA values.
drop_na_rows

Drop rows that are NA in all specified variables.
variance_threshold

Remove variables with near-zero variance.
whiten

Whiten data.