--- title: pipelines keywords: fastai sidebar: home_sidebar nb_path: "01_pipelines.ipynb" ---
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de_dimensions[source]

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

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scatter_plot[source]

scatter_plot(table, output, column1, column2, name1=None, name2=None, logscale=False, value_name='value', sep='\t', hue=None)

scatter plot for two columns in the table

:param str table: table path :param str output: output :param str column1: the column 1 name :param str column2: the column 2 name :param str name1: name in the figure for column1 :param str name2: name in the figure for column2 :param str value_name: the value name, for example: expression, TPM? :param str sep: :param str hue: :param bool logscale: :return:

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