Package: cytominer 0.2.2.9000

Shantanu Singh

cytominer: Methods for Image-Based Cell Profiling

`cytominer` is a suite of common functions used to process high-dimensional readouts from image-based cell profiling experiments.

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

# Install 'cytominer' in R:

install.packages('cytominer', repos = c('https://cytomining.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Last updated from:d911eb5bc1. Checks:7 ERROR, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	ERROR	203
source / vignettes	OK	281
linux-release-x86_64	ERROR	207
macos-release-arm64	ERROR	115
macos-oldrel-arm64	ERROR	160
windows-devel	ERROR	128
windows-release	ERROR	123
windows-oldrel	ERROR	144
wasm-release	OK	161

Last update: 2020-03-24
Started: 2017-09-07

Help page	Topics
Aggregate data based on given grouping.	aggregate
Remove redundant variables.	correlation_threshold
Count the number of 'NA's per variable.	count_na_rows
Compute covariance matrix and vectorize.	covariance
Remove variables with NA values.	drop_na_columns
Drop rows that are 'NA' in all specified variables.	drop_na_rows
Extract subpopulations.	extract_subpopulations
Find significant PC's given the eigenvalues.	find_significant_pcs
Generalized log transform data.	generalized_log
A sparse matrix for sparse random projection.	generate_component_matrix
Husk data.	husk
Mark outlier rows.	mark_outlier_rows
Normalize observation variables.	normalize
Measure replicate correlation of variables.	replicate_correlation
Reduce the dimensionality of a population using sparse random projection.	sparse_random_projection
Spherize data.	spherize
Stratify operations.	stratify
Feature importance based on data entropy.	svd_entropy
Transform observation variables.	transform
Measure variable importance.	variable_importance
Select observation variables.	variable_select
Remove variables with near-zero variance.	variance_threshold