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])
