def get_map(): maps = rc.get_items('map') map_dict = {} for i in range(len(maps)): map_dict[maps[i][0]] = maps[i][1] compares = rc.get_items('compare') compare_groups = [] for i in range(len(compares)): aa = compares[i][1].replace(' ', '').split(',') compare_groups.append([aa[0], aa[1]]) return map_dict, compare_groups
def get_config_info(): paths = rc.get_items('path') maps = rc.get_items('map') map_dict = {} for i in range(len(maps)): map_dict[maps[i][0]] = maps[i][1] compares = rc.get_items('compare') compare_groups = [] for i in range(len(compares)): aa = compares[i][1].replace(' ', '').split(',') compare_groups.append(aa) return paths[0][1], paths[1][1], map_dict, compare_groups
def main(): paths = rc.get_items('path') df = pd.read_csv(os.path.join(paths[1][1], 'combined_otu_table_m3_std.txt'), sep='\t', index_col='OTU ID').drop('taxonomy', axis=1) get_top10(df, paths[1][1])
def main(): paths = rc.get_items('path') map_dict, compare_groups = get_map() df = get_df(map_dict, 'ln') alpha_stats_kruskal(df, compare_groups, paths[1][1]) alpha_stats_ranksums(df, compare_groups, paths[1][1]) alpha_stats_f_oneway(df, compare_groups, paths[1][1]) alpha_stats_mannwhitneyu(df, compare_groups, paths[1][1]) alpha_stats_levene(df, compare_groups, paths[1][1]) alpha_stats_ttest_ind(df, compare_groups, paths[1][1])
def get_df(pre='ln', file='combined_alpha_m1.txt'): paths = rc.get_items('path') df = pd.read_csv(os.path.join(paths[1][1], file), sep='\t', index_col='OTU ID').drop('taxonomy', axis=1).T if pre == 'ln': df = np.log(df + 0.00001) elif pre == 'log10': df = np.log10(df + 0.00001) return df
def get_df(map_dict, pre='ln', file='combined_alpha_m1.txt'): paths = rc.get_items('path') df = pd.read_csv(os.path.join(paths[1][1], file), sep='\t', index_col='Sample') if pre == 'ln': df = np.log(df + 0.00001) elif pre == 'log10': df = np.log10(df + 0.00001) df = df.reset_index() df['Type'] = df['Sample'].map(map_dict) return df
def main(): paths = rc.get_items('path') sort_sample, sort_dict_kegg, tax_kegg, sort_dict_cog, tax_cog = glob_all_otu( paths[0][1]) combined_otu(sort_sample, sort_dict_kegg, tax_kegg, sort_dict_cog, tax_cog, paths[1][1])
def main(): paths = rc.get_items('path') map_dict, _ = get_map() df = get_df(paths[1][1]) get_top50(df, map_dict, paths[1][1])
def main(): paths = rc.get_items('path') sort_sample, sort_dict = glob_all_otu(paths[0][1]) combined_otu(sort_sample, sort_dict, paths[1][1])
def main(): paths = rc.get_items('path') glob_all_otu(paths[0][1], paths[1][1])
def main(): paths = rc.get_items('path') rank_abundance(paths[0][1], paths[1][1])
def main(): paths = rc.get_items('path') get_tax_num(paths[1][1])
def main(): paths = rc.get_items('path') compute_length_ratio(paths[0][1], paths[1][1])
def main(): paths = rc.get_items('path') plot_data = rarefaction_curve(paths[0][1]) draw_plot(plot_data, paths[1][1])
def main(): paths = rc.get_items('path') map_dict, compare_groups = get_map() df = get_df('ln', 'combined_otu_table_m2_std.txt') anosim(df, map_dict, compare_groups, paths[1][1])