def preprocess_otu_table(otu_sample_ids, otu_table, lineages, coords_data, coords_headers, N=0): """Preprocess the OTU table to to generate the required data for the biplots Input: otu_sample_ids: sample identifiers for the otu_table otu_table: contingency table lineages: taxonomic assignments for the OTUs in the otu_table coords_data: principal coordinates data where the taxa will be mapped N: number of most prevalent taxa to keep, by default will use all Output: otu_coords: coordinates representing the N most prevalent taxa in otu_table otu_table: N most prevalent OTUs from the input otu_table otu_lineages: taxonomic assignments corresponding to the N most prevalent OTUs otu_prevalence: vector with the prevalence scores of the N highest values lines: coords where the N most prevalent taxa will be positioned in the biplot """ # return empty values if any of the taxa data is empty if (otu_sample_ids == []) or (otu_table == array([])) or (lineages == []): return [], [], [], [], '' # this means there's only one or fewer rows in the contingency table if len(otu_table) <= 1 or len(lineages) <= 1: raise EmperorUnsupportedComputation, "Biplots are not supported for "+\ "contingency tables with one or fewer rows" # if this element is a list take the first headers and coordinates # both of these will be the master coordinates, i. e. where data is centered if type(coords_data) == list and type(coords_headers) == list: coords_data = coords_data[0] coords_headers = coords_headers[0] # re-arrange the otu table so it matches the order of the samples in the # coordinates data & remove any sample that is not in the coordinates header otu_sample_ids, otu_table = sort_taxa_table_by_pcoa_coords( coords_headers, otu_table, otu_sample_ids) # retrieve the prevalence and the coords prior the filtering prevalence = get_taxa_prevalence(otu_table) bi_plot_coords = get_taxa_coords(otu_table, coords_data) o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence =\ extract_taxa_data(bi_plot_coords, otu_table, lineages, prevalence, N) lines = '\n'.join( make_biplot_scores_output({ 'coord': o_otu_coords, 'lineages': o_otu_lineages })) return o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence, lines
def preprocess_otu_table(otu_sample_ids, otu_table, lineages, coords_data, coords_headers, N=0): """Preprocess the OTU table to to generate the required data for the biplots Input: otu_sample_ids: sample identifiers for the otu_table otu_table: contingency table lineages: taxonomic assignments for the OTUs in the otu_table coords_data: principal coordinates data where the taxa will be mapped N: number of most prevalent taxa to keep, by default will use all Output: otu_coords: coordinates representing the N most prevalent taxa in otu_table otu_table: N most prevalent OTUs from the input otu_table otu_lineages: taxonomic assignments corresponding to the N most prevalent OTUs otu_prevalence: vector with the prevalence scores of the N highest values lines: coords where the N most prevalent taxa will be positioned in the biplot """ # return empty values if any of the taxa data is empty if (otu_sample_ids == []) or (otu_table == array([])) or (lineages == []): return [], [], [], [], '' # this means there's only one or fewer rows in the contingency table if len(otu_table) <= 1 or len(lineages) <= 1: raise EmperorUnsupportedComputation, "Biplots are not supported for "+\ "contingency tables with one or fewer rows" # if this element is a list take the first headers and coordinates # both of these will be the master coordinates, i. e. where data is centered if type(coords_data) == list and type(coords_headers) == list: coords_data = coords_data[0] coords_headers = coords_headers[0] # re-arrange the otu table so it matches the order of the samples in the # coordinates data & remove any sample that is not in the coordinates header otu_sample_ids, otu_table = sort_taxa_table_by_pcoa_coords(coords_headers, otu_table, otu_sample_ids) # retrieve the prevalence and the coords prior the filtering prevalence = get_taxa_prevalence(otu_table) bi_plot_coords = get_taxa_coords(otu_table, coords_data) o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence =\ extract_taxa_data(bi_plot_coords, otu_table, lineages, prevalence, N) lines = '\n'.join(make_biplot_scores_output({'coord': o_otu_coords, 'lineages': o_otu_lineages})) return o_otu_coords, o_otu_table, o_otu_lineages, o_prevalence, lines
def test_make_biplot_scores_output(self): """make_biplot_scores_output correctly formats biplot scores""" taxa = {} taxa['lineages'] = list('ABC') taxa['coord'] = np.array([ [2.1,0.2,0.2,1.4], [1.1,1.2,1.3,1.5], [-.3,-2,2.5,1.9]],float) res = bp.make_biplot_scores_output(taxa) exp = ['#Taxon\tpc1\tpc2\tpc3\tpc4', 'A\t2.1\t0.2\t0.2\t1.4', 'B\t1.1\t1.2\t1.3\t1.5', 'C\t-0.3\t-2.0\t2.5\t1.9', ] self.assertEqual(res, exp)
def test_make_biplot_scores_output(self): """make_biplot_scores_output correctly formats biplot scores""" taxa = {} taxa['lineages'] = list('ABC') taxa['coord'] = np.array( [[2.1, 0.2, 0.2, 1.4], [1.1, 1.2, 1.3, 1.5], [-.3, -2, 2.5, 1.9]], float) res = bp.make_biplot_scores_output(taxa) exp = [ '#Taxon\tpc0\tpc1\tpc2\tpc3', 'A\t2.1\t0.2\t0.2\t1.4', 'B\t1.1\t1.2\t1.3\t1.5', 'C\t-0.3\t-2.0\t2.5\t1.9', ] self.assertEqual(res, exp)