flotilla.test.conftest module

This file will be auto-imported for every testing session, so you can use these objects and functions across test files.

flotilla.test.conftest.RANDOM_STATE()[source]

Consistent random state

flotilla.test.conftest.base_data(expression_data)[source]
flotilla.test.conftest.boolean_gene_categories()[source]
flotilla.test.conftest.color_ordered(group_order, group_to_color)[source]

Colors in the order created by the groups

flotilla.test.conftest.colors(n_groups)[source]

Colors to use for the samples

flotilla.test.conftest.data_dir()[source]
flotilla.test.conftest.df_nonneg(df_norm)[source]

Non-negative data for testing NMF

flotilla.test.conftest.df_norm(x_norm)[source]

Normally distributed pandas dataframe

flotilla.test.conftest.event_name()[source]
flotilla.test.conftest.events(n_events)[source]
flotilla.test.conftest.expression_data(samples, genes, groupby, na_thresh)[source]
flotilla.test.conftest.expression_data_no_na(samples, genes, groupby)[source]
flotilla.test.conftest.expression_feature_data(genes, gene_categories, boolean_gene_categories, renamed)[source]
flotilla.test.conftest.expression_feature_rename_col(renamed)[source]
flotilla.test.conftest.expression_kws(expression_feature_data, expression_feature_rename_col, expression_log_base, expression_plus_one, expression_thresh)[source]
flotilla.test.conftest.expression_log_base()[source]
flotilla.test.conftest.expression_plus_one()[source]
flotilla.test.conftest.expression_thresh(request)[source]
flotilla.test.conftest.feature_ids(request, base_data)[source]
flotilla.test.conftest.feature_subset(request)[source]
flotilla.test.conftest.featurewise(request)[source]
flotilla.test.conftest.gene_categories()[source]
flotilla.test.conftest.gene_name()[source]
flotilla.test.conftest.gene_ontology_data(gene_ontology_data_path)[source]
flotilla.test.conftest.gene_ontology_data_path(data_dir)[source]
flotilla.test.conftest.genelist_path(data_dir)[source]
flotilla.test.conftest.genes(n_genes)[source]
flotilla.test.conftest.group_order(groups)[source]

so-called ‘logical’ order of groups for plotting.

To test if the user gave a specific order of the phenotypes, e.g. by differentiation time

flotilla.test.conftest.group_to_color(group_order, colors)[source]

Mapping of groups to colors

flotilla.test.conftest.group_to_marker(request)[source]

Mapping of groups to plotting markers

flotilla.test.conftest.group_transitions(group_order)[source]

List of pairwise transitions between phenotypes, for NMF

flotilla.test.conftest.groupby(groups, samples)[source]
flotilla.test.conftest.groups(n_groups)[source]

Phenotype group names

flotilla.test.conftest.mapping_stats_data(samples, technical_outliers, mapping_stats_min_reads_default, mapping_stats_number_mapped_col)[source]
flotilla.test.conftest.mapping_stats_kws(mapping_stats_number_mapped_col)[source]
flotilla.test.conftest.mapping_stats_min_reads_default()[source]
flotilla.test.conftest.mapping_stats_number_mapped_col()[source]
flotilla.test.conftest.metadata_data(groupby, samples, n_samples)[source]
flotilla.test.conftest.metadata_kws(group_order, group_to_color, group_to_marker)[source]
flotilla.test.conftest.metadata_minimum_samples(request)[source]
flotilla.test.conftest.modality_models()[source]
flotilla.test.conftest.n_events()[source]
flotilla.test.conftest.n_genes()[source]
flotilla.test.conftest.n_groups()[source]

Number of phenotype groups.

flotilla.test.conftest.n_samples()[source]

Number of samples to create example data from

flotilla.test.conftest.na_thresh(request)[source]
flotilla.test.conftest.outliers(request, n_samples, samples)[source]

If request.param is True, return randomly chosen samples as outliers, otherwise None

flotilla.test.conftest.pooled(request, n_samples, samples)[source]

If request.param is True, return randomly chosen samples as pooled, otherwise None

flotilla.test.conftest.renamed(request)[source]
flotilla.test.conftest.sample_ids(request, base_data)[source]
flotilla.test.conftest.sample_subset(request, samples)[source]
flotilla.test.conftest.samples(n_samples)[source]

Sample ids

flotilla.test.conftest.splicing(splicing_data)[source]
flotilla.test.conftest.splicing_data(samples, events, true_modalities, modality_models, groupby)[source]
flotilla.test.conftest.splicing_feature_common_id(gene_name)[source]
flotilla.test.conftest.splicing_feature_data(events, genes, gene_name, expression_feature_data, splicing_feature_common_id)[source]
flotilla.test.conftest.splicing_kws(splicing_feature_data, splicing_feature_common_id, gene_name)[source]
flotilla.test.conftest.standardize(request)[source]
flotilla.test.conftest.technical_outliers(n_samples, samples)[source]

If request.param is True, return randomly chosen samples as technical outliers, otherwise None

flotilla.test.conftest.true_modalities(events, modality_models, groups)[source]
flotilla.test.conftest.x_norm()[source]

Normally distributed numpy array

Olga B. Botvinnik is funded by the NDSEG fellowship and is a NumFOCUS John Hunter Technology Fellow.
Michael T. Lovci was partially funded by a fellowship from Genentech.
Partially funded by NIH grants NS075449 and HG004659 and CIRM grants RB4-06045 and TR3-05676 to Gene Yeo.