generate_component_matrix

the dimensionality of the original space.

n_features

the dimensionality of the projected space.

n_components

the density of the sparse random matrix.

density

<code>generate_component_matrix</code> generates the sparse random component matrix
for performing sparse random projection. If <code>density</code> is the density of
the sparse matrix and <code>n_components</code> is the size of the projected space,
the elements of the random matrix are drawn from

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>.

Shantanu Singh

generate_component_matrix: A sparse matrix for sparse random projection.

Description

Usage

Arguments

Value

Details

Examples