de_dimensions
(matrix
, prefix
, sep
='\t'
, dedimensions_method
='PCA'
, cluster_method
='MiniBatchKMeans'
, assess_method
='silhouette_score'
, dimensions
=3
, cluster_number
=None
, row_feature
=True
, annotation
=None
, size
=None
, style
=None
, title
=None
, fig
='png'
)
:param str matrix: matrix table, if row represents feature, please note to add '--row-feature' option
:param str prefix: output prefix
:param str sep: separation
:param str dedimensions_method: de-dimensions method
:param str cluster_method: cluster method
:param int dimensions: reduce to n dimensions
:param int cluster_number: cluster number, if not specific it, it will be the best cluster number infered
:param bool row_feature: row in the matrix represents feature
:param str assess_method: assess methods for best cluster number
:param str annotation: annotation file, sep should be ','
:param str size: size column in annotation file
:param str style: style column in annotation file
:param str title: figure title
:param str fig: png/pdf