def test_unequal_group_sizes(self):
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 6, 3, -0.363636, 0.878, 999])
np.random.seed(0)
obs = anosim(self.dm_unequal, self.grouping_unequal)
self.assert_series_equal(obs, exp)
np.random.seed(0)
obs = anosim(self.dm_unequal, self.grouping_unequal_relabeled)
self.assert_series_equal(obs, exp)
def test_unequal_group_sizes(self):
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 6, 3, -0.363636, 0.878, 999])
np.random.seed(0)
obs = anosim(self.dm_unequal, self.grouping_unequal)
self.assert_series_equal(obs, exp)
np.random.seed(0)
obs = anosim(self.dm_unequal, self.grouping_unequal_relabeled)
self.assert_series_equal(obs, exp)
def test_no_ties(self):
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 4, 2, 0.625, 0.332, 999],
name='ANOSIM results')
np.random.seed(0)
obs = anosim(self.dm_no_ties, self.grouping_equal)
self.assert_series_equal(obs, exp)
def test_ties(self):
# Ensure we get the same results if we rerun the method using the same
# inputs. Also ensure we get the same results if we run the method
# using a grouping vector or a data frame with equivalent groupings.
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 4, 2, 0.25, 0.671, 999])
for _ in range(2):
np.random.seed(0)
obs = anosim(self.dm_ties, self.grouping_equal)
self.assert_series_equal(obs, exp)
for _ in range(2):
np.random.seed(0)
obs = anosim(self.dm_ties, self.df, column='Group')
self.assert_series_equal(obs, exp)
def test_no_ties(self):
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 4, 2, 0.625, 0.332, 999],
name='ANOSIM results')
np.random.seed(0)
obs = anosim(self.dm_no_ties, self.grouping_equal)
self.assert_series_equal(obs, exp)
def test_ties(self):
# Ensure we get the same results if we rerun the method using the same
# inputs. Also ensure we get the same results if we run the method
# using a grouping vector or a data frame with equivalent groupings.
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 4, 2, 0.25, 0.671, 999])
for _ in range(2):
np.random.seed(0)
obs = anosim(self.dm_ties, self.grouping_equal)
self.assert_series_equal(obs, exp)
for _ in range(2):
np.random.seed(0)
obs = anosim(self.dm_ties, self.df, column='Group')
self.assert_series_equal(obs, exp)
def beta_diversity_pcoa(biom_fp, method="braycurtis", permutations=99, dim=2,
col='method', colormap={'expected': 'red',
'rdp': 'seagreen',
'sortmerna': 'gray',
'uclust': 'blue',
'blast': 'purple'}):
'''From biom table, compute Bray-Curtis distance; generate PCoA plot;
and calculate adonis differences.
biom_fp: path
Path to biom.Table containing sample metadata.
method: str
skbio.Diversity method to use for ordination.
permutations: int
Number of permutations to perform for anosim tests.
dim: int
Number of dimensions to plot. Currently supports only 2-3 dimensions.
col: str
metadata name to use for distinguishing groups for anosim tests and
pcoa plots.
colormap: dict
map groups names (must be group names in col) to colors used for plots.
'''
dm, s_md = make_distance_matrix(biom_fp, method=method)
# pcoa
pc = pcoa(dm)
# anosim tests
results = anosim(dm, s_md, column=col, permutations=permutations)
print('R = ', results['test statistic'], '; P = ', results['p-value'])
if dim == 2:
# bokeh pcoa plots
pc123 = pc.samples.ix[:, ["PC1", "PC2", "PC3"]]
smd_merge = s_md.merge(pc123, left_index=True, right_index=True)
smd_merge['Color'] = [colormap[x] for x in smd_merge['method']]
title = smd_merge['reference'][0]
labels = ['PC {0} ({1:.2f})'.format(d + 1, pc.proportion_explained[d])
for d in range(0, 2)]
circle_plot_from_dataframe(smd_merge, "PC1", "PC2", title,
columns=["method", "sample_id", "params"],
color="Color", labels=labels)
else:
# skbio pcoa plots
pcoa_plot_skbio(pc, s_md, col='method')
return s_md, results, pc, dm
def main(args):
data_df = pd.read_table(args.data, index_col=0)
data_df_nonnull = data_df[data_df['taxon'].notnull()]
val_cols = data_df_nonnull.columns
val_cols.remove('taxon')
dm = DistanceMatrix(squareform(pdist(data_df_nonnull[val_cols], metric='euclidean')))
a = anosim(dm, data_df_nonnull['taxon'], permutations=0)
a_df = pd.DataFrame(a).T
a_df.index = [args.data_name]
a_df.to_csv(sys.stdout, header=None)
eigen3 = eigen['PC3'].values
print(eigen)
print(eigen1)
df_fins = pd.read_csv("samples_id_all.tsv", sep="\t", header=0, index_col=0)
print(df_fins)
df_fins.reset_index()
df_fins = df_fins[['sal']]
print(df_fins)
#df_fin.to_csv("test6.tsv", sep="\t", header=1)
df_fins['Observed OTUs'] = adiv_obs_otuss
df_fins['Faith PD'] = adiv_faith_pds
anosims = anosim(wu_dms, df_fins, column='sal', permutations=999)
print(anosims['test statistic'])
print(anosims['p-value'])
print(df_fins.corr(method="spearman"))
print(adiv_obs_otuss)
figs = plt.figure()
#plt.close('all')
#plt.subplot(1,3,1)
figs = wu_pcs.plot(df_fins,
'sal',
axis_labels=('PC1' + str(eigen1) + '%',
'PC2' + str(eigen2) + '%',
'PC3' + str(eigen3) + '%'),
def beta_diversity(TaXon_table_xlsx, width, heigth, cmap, meta_data_to_test,
taxonomic_level, path_to_outdirs, template, font_size,
diss_metric):
import pandas as pd
import numpy as np
from skbio.diversity import beta_diversity
from skbio.stats.distance import anosim
import plotly.express as px
from pathlib import Path
import PySimpleGUI as sg
import webbrowser
TaXon_table_xlsx = Path(TaXon_table_xlsx)
Meta_data_table_xlsx = Path(
str(path_to_outdirs) + "/" + "Meta_data_table" + "/" +
TaXon_table_xlsx.stem + "_metadata.xlsx")
TaXon_table_df = pd.read_excel(TaXon_table_xlsx,
header=0).fillna("unidentified")
TaXon_table_samples = TaXon_table_df.columns.tolist()[10:]
Meta_data_table_df = pd.read_excel(Meta_data_table_xlsx,
header=0).fillna("nan")
Meta_data_table_samples = Meta_data_table_df['Samples'].tolist()
metadata_list = Meta_data_table_df[meta_data_to_test].values.tolist()
metadata_loc = Meta_data_table_df.columns.tolist().index(meta_data_to_test)
## drop samples with metadata called nan (= empty)
drop_samples = [
i[0] for i in Meta_data_table_df.values.tolist()
if i[metadata_loc] == "nan"
]
if drop_samples != []:
## filter the TaXon table
TaXon_table_df = TaXon_table_df.drop(drop_samples, axis=1)
TaXon_table_samples = TaXon_table_df.columns.tolist()[10:]
## also remove empty OTUs
row_filter_list = []
for row in TaXon_table_df.values.tolist():
reads = set(row[10:])
if reads != {0}:
row_filter_list.append(row)
columns = TaXon_table_df.columns.tolist()
TaXon_table_df = pd.DataFrame(row_filter_list, columns=columns)
Meta_data_table_df = pd.DataFrame(
[
i for i in Meta_data_table_df.values.tolist()
if i[0] not in drop_samples
],
columns=Meta_data_table_df.columns.tolist())
Meta_data_table_samples = Meta_data_table_df['Samples'].tolist()
metadata_list = Meta_data_table_df[meta_data_to_test].values.tolist()
## create a y axis title text
taxon_title = taxonomic_level
## adjust taxonomic level if neccessary
if taxonomic_level in ["ASVs", "ESVs", "OTUs", "zOTUs"]:
taxon_title = taxonomic_level
taxonomic_level = "ID"
# check if the meta data differs
if len(set(Meta_data_table_df[meta_data_to_test])) == len(
Meta_data_table_df['Samples'].tolist()):
sg.Popup(
"The meta data is unique for all samples. Please adjust the meta data table!",
title=("Error"))
raise RuntimeError
# check if the meta data differs
if len(set(Meta_data_table_df[meta_data_to_test])) == 1:
sg.Popup(
"The meta data is similar for all samples. Please adjust the meta data table!",
title=("Error"))
raise RuntimeError
if sorted(TaXon_table_samples) == sorted(Meta_data_table_samples):
## collect samples for plot
samples = Meta_data_table_samples
## extract the relevant data
TaXon_table_df = TaXon_table_df[[taxonomic_level] + samples]
## define an aggregation function to combine multiple hit of one taxonimic level
aggregation_functions = {}
## define samples functions
for sample in samples:
## 'sum' will calculate the sum of p/a data
aggregation_functions[sample] = 'sum'
## define taxon level function
aggregation_functions[taxonomic_level] = 'first'
## create condensed dataframe
df_new = TaXon_table_df.groupby(
TaXon_table_df[taxonomic_level]).aggregate(aggregation_functions)
if 'unidentified' in df_new.index:
df_new = df_new.drop('unidentified')
## collect reads
data = df_new[samples].transpose().values.tolist()
## calculate dissimilarity distances
dissimilarity_dm = beta_diversity(diss_metric, data, samples)
anosim_results = anosim(dissimilarity_dm,
metadata_list,
permutations=999)
anosim_r = round(anosim_results['test statistic'], 5)
anosim_p = anosim_results['p-value']
textbox = "Anosim (" + meta_data_to_test + ", " + taxon_title + ")<br>" + "R = " + str(
anosim_r) + "<br>" + "p = " + str(anosim_p)
matrix = dissimilarity_dm.data
matrix_df = pd.DataFrame(matrix)
matrix_df.columns = samples
matrix_df.index = samples
# create plot
color_label = diss_metric + " distance"
fig = px.imshow(matrix,
x=samples,
y=samples,
color_continuous_scale=cmap,
labels=dict(color=color_label))
fig.update_layout(height=int(heigth),
width=int(width),
template=template,
showlegend=True,
title=textbox,
font_size=font_size,
title_font_size=font_size)
# finish script
output_pdf = Path(
str(path_to_outdirs) + "/" + "Beta_diversity" + "/" +
TaXon_table_xlsx.stem + "_" + meta_data_to_test + "_" +
taxon_title + "_" + diss_metric + ".pdf")
output_html = Path(
str(path_to_outdirs) + "/" + "Beta_diversity" + "/" +
TaXon_table_xlsx.stem + "_" + meta_data_to_test + "_" +
taxon_title + "_" + diss_metric + ".html")
output_xlsx = Path(
str(path_to_outdirs) + "/" + "Beta_diversity" + "/" +
TaXon_table_xlsx.stem + "_" + meta_data_to_test + "_" +
taxon_title + "_" + diss_metric + ".xlsx")
fig.write_image(str(output_pdf))
fig.write_html(str(output_html))
matrix_df.to_excel(output_xlsx)
## ask to show plot
answer = sg.PopupYesNo('Show plot?', keep_on_top=True)
if answer == "Yes":
webbrowser.open('file://' + str(output_html))
## write to log file
sg.Popup("Beta diversity estimate are found in",
path_to_outdirs,
"/Beta_diversity/",
title="Finished",
keep_on_top=True)
from taxontabletools.create_log import ttt_log
ttt_log("beta diversity", "analysis", TaXon_table_xlsx.name,
output_pdf.name, meta_data_to_test, path_to_outdirs)
else:
sg.PopupError(
"Error: The samples between the taxon table and meta table do not match!",
keep_on_top=True)
eigen3 = eigen['PC3'].values
print(eigen)
print(eigen1)
df_fin = pd.read_csv("samples_id_all.tsv", sep="\t", header=0, index_col=0)
print(df_fin)
df_fin.reset_index()
df_fin = df_fin[['true_lat']]
print(df_fin)
#df_fin.to_csv("test6.tsv", sep="\t", header=1)
df_fin['Observed OTUs'] = adiv_obs_otus
df_fin['Faith PD'] = adiv_faith_pd
anosim_lat = anosim(wu_dm, df_fin, column='true_lat', permutations=999)
print(anosim_lat['test statistic'])
print(anosim_lat['p-value'])
print(df_fin.corr(method="spearman"))
print(adiv_obs_otus)
fig = plt.figure()
#plt.close('all')
#plt.subplot(1,3,1)
fig = wu_pc.plot(df_fin,
'true_lat',
axis_labels=('PC1' + str(eigen1) + '%',
'PC2' + str(eigen2) + '%',
'PC3' + str(eigen3) + '%'),
its - 1][k].cluster_label
field_plot = np.ma.masked_array(field_plot, field_plot == -10000)
#%% Determine from which clusters the data is part of:
nomask = np.where(~field_plot.mask)
field_plot = field_plot[nomask]
args = nwf.find_nearest_args(vLons[nomask], vLats[nomask], Flats,
Flons)
Flabels = field_plot[args]
args = nwf.find_nearest_args(vLons[nomask], vLats[nomask], FlatsDino,
FlonsDino)
Dinolabels = field_plot[args]
#%%
if (len(np.unique(Dinolabels)) > 1):
Dano = anosim(DistanceMatrix(Dinotaxdist),
Dinolabels.astype(str),
permutations=perm)
DinoP[its] = list(Dano)[5]
DinoR[its] = list(Dano)[4]
if (len(np.unique(Flabels)) > 1):
Fano = anosim(DistanceMatrix(Ftaxdist),
Flabels.astype(str),
permutations=perm)
FP[its] = list(Fano)[5]
FR[its] = list(Fano)[4]
#%% Save file with ANOSIM results
np.savez('ANOSIM_hierarchicalclus%s_sp%d_perm%d_its%d_mlat%d.npz' %
(season, sp, perm, iterations, maxlat),
ForamP=FP,
DinoP=DinoP,
sample_id = each_sample_split[0]
sample_group = each_sample_split[1]
sample_id_list.append(sample_id)
sample_group_list.append(sample_group)
# read in data as dataframe
df = pd.read_csv(infile_data, sep='\t')
# get list of list from dataframe
lol_data_in = []
for col_id in sample_id_list:
column_num_list = (df[col_id].values).tolist()
lol_data_in.append(column_num_list)
# calculate distance matrix
dist_arrary = pairwise_distances(lol_data_in,
lol_data_in,
metric=distance_metric)
# add sample id to distance matrix
dist_matrix = DistanceMatrix(dist_arrary, sample_id_list)
# perform anosim test
anosim_test = anosim(dist_matrix, sample_group_list, permutations=999)
print(anosim_test)
print()
# perform permanova test
permanova_test = permanova(dist_matrix, sample_group_list, permutations=999)
print(permanova_test)
eigen3 = eigen['PC3'].values
print(eigen)
print(eigen1)
df_fin = pd.read_csv("samples_id_all.tsv", sep="\t", header=0, index_col=0)
print(df_fin)
df_fin.reset_index()
df_fin = df_fin[['region']]
print(df_fin)
#df_fin.to_csv("test6.tsv", sep="\t", header=1)
df_fin['Observed OTUs'] = adiv_obs_otus
df_fin['Faith PD'] = adiv_faith_pd
anosim = anosim(wu_dm, df_fin, column='region', permutations=999)
print(anosim['test statistic'])
print(anosim['p-value'])
print(df_fin.corr(method="spearman"))
#print(adiv_obs_otus)
fig = plt.figure()
#plt.close('all')
#plt.subplot(1,3,1)
fig = wu_pc.plot(df_fin,
'region',
axis_labels=('PC1' + str(eigen1) + '%',
'PC2' + str(eigen2) + '%',
'PC3' + str(eigen3) + '%'),
def test_no_permutations(self):
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 4, 2, 0.625, np.nan, 0],
name='ANOSIM results')
obs = anosim(self.dm_no_ties, self.grouping_equal, permutations=0)
self.assert_series_equal(obs, exp)
eigen3 = eigen['PC3'].values
print(eigen)
print(eigen1)
df_fint = pd.read_csv("samples_id_all.tsv", sep="\t", header=0, index_col=0)
print(df_fint)
df_fint.reset_index()
df_fint = df_fint[['temp']]
print(df_fint)
#df_fin.to_csv("test6.tsv", sep="\t", header=1)
df_fint['Observed OTUs'] = adiv_obs_otust
df_fint['Faith PD'] = adiv_faith_pdt
anosimt = anosim(wu_dmt, df_fint, column='temp', permutations=999)
print(anosimt['test statistic'])
print(anosimt['p-value'])
print(df_fint.corr(method="spearman"))
print(adiv_obs_otust)
figt = plt.figure()
#plt.close('all')
#plt.subplot(1,3,1)
figt = wu_pct.plot(df_fint,
'temp',
axis_labels=('PC1' + str(eigen1) + '%',
'PC2' + str(eigen2) + '%',
'PC3' + str(eigen3) + '%'),
for a in range(len(rows[0])):
if a > 0:
this_sample = []
for b in range(len(rows)):
if b > 0:
this_sample.append(float(rows[b][a]))
samples.append(this_sample)
"""
only_samples = ['LR', 'SR']
new_samples, new_names = [], []
for a in range(len(sample_names)):
for b in range(len(only_samples)):
if sample_names[a] == only_samples[b]:
new_samples.append(samples[a])
new_names.append(sample_names[a])
samples = new_samples
sample_names = new_names
print(len(samples), len(sample_names))
"""
sam_dm = dm.from_iterable(samples, metric=braycurtis)
pdisp = permdisp(sam_dm,
sample_names,
column=None,
test='median',
permutations=999)
print(pdisp)
asim = anosim(sam_dm, sample_names, column=None, permutations=999)
print(asim)
perm = permanova(sam_dm, sample_names, column=None, permutations=999)
print(perm)
def test_no_permutations(self):
exp = pd.Series(index=self.exp_index,
data=['ANOSIM', 'R', 4, 2, 0.625, np.nan, 0],
name='ANOSIM results')
obs = anosim(self.dm_no_ties, self.grouping_equal, permutations=0)
self.assert_series_equal(obs, exp)
eigen3 = eigen['PC3'].values
print(eigen)
print(eigen1)
df_find = pd.read_csv("samples_id_all.tsv", sep="\t", header=0, index_col=0)
print(df_find)
df_find.reset_index()
df_find = df_find[['depth_group_50']]
print(df_find)
#df_fin.to_csv("test6.tsv", sep="\t", header=1)
df_find['Observed OTUs'] = adiv_obs_otusd
df_find['Faith PD'] = adiv_faith_pdd
anosimd = anosim(wu_dmd, df_find, column='depth_group_50', permutations=999)
print(anosimd['test statistic'])
print(anosimd['p-value'])
print(df_find.corr(method="spearman"))
print(adiv_obs_otusd)
figd = plt.figure()
#plt.close('all')
#plt.subplot(1,3,1)
figd = wu_pcd.plot(df_find,
'depth_group_50',
axis_labels=('PC1' + str(eigen1) + '%',
'PC2' + str(eigen2) + '%',
'PC3' + str(eigen3) + '%'),
def PCoA_analysis(TaXon_table_xlsx, meta_data_to_test, taxonomic_level, width,
height, pcoa_s, path_to_outdirs, template, font_size,
color_discrete_sequence, pcoa_dissimilarity):
import pandas as pd
import numpy as np
from skbio.diversity import beta_diversity
from skbio.stats.ordination import pcoa
from skbio.stats.distance import anosim
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.express as px
from pathlib import Path
import PySimpleGUI as sg
import os, webbrowser
from itertools import combinations
TaXon_table_xlsx = Path(TaXon_table_xlsx)
Meta_data_table_xlsx = Path(
str(path_to_outdirs) + "/" + "Meta_data_table" + "/" +
TaXon_table_xlsx.stem + "_metadata.xlsx")
TaXon_table_df = pd.read_excel(TaXon_table_xlsx,
header=0).fillna("unidentified")
TaXon_table_samples = TaXon_table_df.columns.tolist()[10:]
Meta_data_table_df = pd.read_excel(Meta_data_table_xlsx,
header=0).fillna("nan")
Meta_data_table_samples = Meta_data_table_df['Samples'].tolist()
metadata_list = Meta_data_table_df[meta_data_to_test].values.tolist()
metadata_loc = Meta_data_table_df.columns.tolist().index(meta_data_to_test)
## drop samples with metadata called nan (= empty)
drop_samples = [
i[0] for i in Meta_data_table_df.values.tolist()
if i[metadata_loc] == "nan"
]
if drop_samples != []:
## filter the TaXon table
TaXon_table_df = TaXon_table_df.drop(drop_samples, axis=1)
TaXon_table_samples = TaXon_table_df.columns.tolist()[10:]
## also remove empty OTUs
row_filter_list = []
for row in TaXon_table_df.values.tolist():
reads = set(row[10:])
if reads != {0}:
row_filter_list.append(row)
columns = TaXon_table_df.columns.tolist()
TaXon_table_df = pd.DataFrame(row_filter_list, columns=columns)
Meta_data_table_df = pd.DataFrame(
[
i for i in Meta_data_table_df.values.tolist()
if i[0] not in drop_samples
],
columns=Meta_data_table_df.columns.tolist())
Meta_data_table_samples = Meta_data_table_df['Samples'].tolist()
## create a y axis title text
taxon_title = taxonomic_level.lower()
## adjust taxonomic level if neccessary
if taxonomic_level in ["ASVs", "ESVs", "OTUs", "zOTUs"]:
taxon_title = taxonomic_level
taxonomic_level = "ID"
# check if the meta data differs
if len(set(Meta_data_table_df[meta_data_to_test])) == len(
Meta_data_table_df['Samples'].tolist()):
sg.Popup(
"The meta data is unique for all samples. Please adjust the meta data table!",
title=("Error"))
raise RuntimeError
# check if the meta data differs
if len(set(Meta_data_table_df[meta_data_to_test])) == 1:
sg.Popup(
"The meta data is similar for all samples. Please adjust the meta data table!",
title=("Error"))
raise RuntimeError
if sorted(TaXon_table_samples) == sorted(Meta_data_table_samples):
samples = Meta_data_table_samples
## extract the relevant data
TaXon_table_df = TaXon_table_df[[taxonomic_level] + samples]
## define an aggregation function to combine multiple hit of one taxonimic level
aggregation_functions = {}
## define samples functions
for sample in samples:
## 'sum' will calculate the sum of p/a data
aggregation_functions[sample] = 'sum'
## define taxon level function
aggregation_functions[taxonomic_level] = 'first'
## create condensed dataframe
TaXon_table_df = TaXon_table_df.groupby(
TaXon_table_df[taxonomic_level]).aggregate(aggregation_functions)
if 'unidentified' in TaXon_table_df.index:
TaXon_table_df = TaXon_table_df.drop('unidentified')
data = TaXon_table_df[samples].transpose().values.tolist()
jc_dm = beta_diversity(pcoa_dissimilarity, data, samples)
ordination_result = pcoa(jc_dm)
metadata_list = Meta_data_table_df[meta_data_to_test].values.tolist()
anosim_results = anosim(jc_dm, metadata_list, permutations=999)
anosim_r = round(anosim_results['test statistic'], 5)
anosim_p = anosim_results['p-value']
textbox = meta_data_to_test + ", " + taxon_title + "<br>Anosim " + "R = " + str(
anosim_r) + " " + "p = " + str(anosim_p)
#######################################################################################
# create window to ask for PCoA axis to test
def slices(list, slice):
for i in range(0, len(list), slice):
yield list[i:i + slice]
# collect the PCoA proportion explained values
proportion_explained_list = []
for i, pcoa_axis in enumerate(ordination_result.proportion_explained):
if round(pcoa_axis * 100, 2) >= 1:
proportion_explained_list.append("PC" + str(i + 1) + " (" +
str(round(pcoa_axis *
100, 2)) + " %)")
pcoa_axis_checkboxes = list(
slices([
sg.Checkbox(name, key=name, size=(15, 1))
for name in proportion_explained_list
], 10))
pcoa_window_layout = [
[sg.Text('Check up to four axes to be displayed')],
[sg.Frame(layout=pcoa_axis_checkboxes, title='')],
[sg.Text('Only axes >= 1 % explained variance are shown')],
[sg.CB("Connect categories", default=True, key="draw_mesh")],
[sg.Text('')],
[sg.Button('Plot', key='Plot')],
[sg.Button('Back')],
]
pcoa_window = sg.Window('PCoA axis',
pcoa_window_layout,
keep_on_top=True)
while True:
event, values = pcoa_window.read()
draw_mesh = values["draw_mesh"]
if event is None or event == 'Back':
break
if event == 'Plot':
## create a subfolder for better sorting and overview
dirName = Path(
str(path_to_outdirs) + "/" + "PCoA_plots" + "/" +
TaXon_table_xlsx.stem + "/")
if not os.path.exists(dirName):
os.mkdir(dirName)
# collect the pcoa axis values
axis_to_plot = [
key for key, value in values.items()
if value == True and "PC" in key
]
# pass on only if two pcoa axes were checked
if len(axis_to_plot) == 2:
cat1 = axis_to_plot[1].split()[0]
cat2 = axis_to_plot[0].split()[0]
df_pcoa = ordination_result.samples[[cat1, cat2]]
df_pcoa.insert(
2, "Metadata",
Meta_data_table_df[meta_data_to_test].values.tolist(),
True)
df_pcoa.insert(
3, "Samples",
Meta_data_table_df["Samples"].values.tolist(), True)
if draw_mesh == True:
combinations_list = []
for metadata in df_pcoa["Metadata"]:
## collect all entries for the respective metadata
arr = df_pcoa.loc[df_pcoa['Metadata'] == metadata][
[cat1, cat2, "Metadata",
"Samples"]].to_numpy()
## create a df for all possible combinations using itertools combinations
for entry in list(combinations(arr, 2)):
combinations_list.append(list(entry[0]))
combinations_list.append(list(entry[1]))
## create a dataframe to draw the plot from
df = pd.DataFrame(combinations_list)
df.columns = [cat1, cat2, "Metadata", "Samples"]
fig = px.scatter(
df,
x=cat1,
y=cat2,
color="Metadata",
text="Samples",
title=textbox,
color_discrete_sequence=color_discrete_sequence)
fig.update_traces(marker_size=int(pcoa_s),
mode="markers+lines")
fig.update_layout(height=int(height),
width=int(width),
template=template,
showlegend=True,
font_size=font_size,
title_font_size=font_size)
fig.update_xaxes(title=axis_to_plot[1])
fig.update_yaxes(title=axis_to_plot[0])
else:
fig = px.scatter(
df_pcoa,
x=cat1,
y=cat2,
color="Metadata",
text="Samples",
title=textbox,
color_discrete_sequence=color_discrete_sequence)
fig.update_traces(marker_size=int(pcoa_s),
mode="markers")
fig.update_layout(height=int(height),
width=int(width),
template=template,
showlegend=True,
font_size=font_size,
title_font_size=font_size)
fig.update_xaxes(title=axis_to_plot[1])
fig.update_yaxes(title=axis_to_plot[0])
## define output files
output_pdf = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + ".pdf")
output_html = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + ".html")
output_xlsx = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + ".xlsx")
## write files
fig.write_image(str(output_pdf))
fig.write_html(str(output_html))
ordination_result.samples[[cat1,
cat2]].to_excel(output_xlsx)
## ask to show file
answer = sg.PopupYesNo('Show plot?', keep_on_top=True)
if answer == "Yes":
webbrowser.open('file://' + str(output_html))
## print closing text
closing_text = "\n" + "PCoA plots are found in: " + str(
path_to_outdirs) + "/PCoA_plots/"
sg.Popup(closing_text, title="Finished", keep_on_top=True)
## write to log
from taxontabletools.create_log import ttt_log
ttt_log("pcoa analysis", "analysis", TaXon_table_xlsx.name,
output_pdf.name, meta_data_to_test,
path_to_outdirs)
break
elif len(axis_to_plot) == 3:
cat1 = axis_to_plot[0].split()[0]
cat2 = axis_to_plot[1].split()[0]
cat3 = axis_to_plot[2].split()[0]
df_pcoa = ordination_result.samples[[cat1, cat2, cat3]]
df_pcoa.insert(
3, "Metadata",
Meta_data_table_df[meta_data_to_test].values.tolist(),
True)
df_pcoa.insert(
4, "Samples",
Meta_data_table_df["Samples"].values.tolist(), True)
## check if lines are to be drawn between the dots
if draw_mesh == True:
combinations_list = []
for metadata in df_pcoa["Metadata"]:
## collect all entries for the respective metadata
arr = df_pcoa.loc[df_pcoa['Metadata'] == metadata][
[cat1, cat2, cat3, "Metadata",
"Samples"]].to_numpy()
## create a df for all possible combinations using itertools combinations
for entry in list(combinations(arr, 2)):
combinations_list.append(list(entry[0]))
combinations_list.append(list(entry[1]))
## create a dataframe to draw the plot from
df = pd.DataFrame(combinations_list)
df.columns = [cat1, cat2, cat3, "Metadata", "Samples"]
## draw the plot
fig = px.scatter_3d(
df,
x=cat1,
y=cat2,
z=cat3,
color="Metadata",
text="Samples",
title=textbox,
color_discrete_sequence=color_discrete_sequence)
fig.update_traces(marker_size=int(pcoa_s),
mode="markers+lines",
line=dict(width=0.5))
fig.update_layout(height=int(height),
width=int(width),
template=template,
title=textbox,
showlegend=True,
font_size=font_size,
title_font_size=font_size)
fig.update_layout(
scene=dict(xaxis_title=axis_to_plot[0],
yaxis_title=axis_to_plot[1],
zaxis_title=axis_to_plot[2]))
else:
fig = px.scatter_3d(
df_pcoa,
x=cat1,
y=cat2,
z=cat3,
color="Metadata",
text="Samples",
color_discrete_sequence=color_discrete_sequence)
fig.update_traces(marker_size=int(pcoa_s),
mode="markers")
fig.update_layout(height=int(height),
width=int(width),
template=template,
showlegend=True,
title=textbox,
font_size=font_size,
title_font_size=font_size)
fig.update_layout(
scene=dict(xaxis_title=axis_to_plot[0],
yaxis_title=axis_to_plot[1],
zaxis_title=axis_to_plot[2]))
## define output files
output_pdf = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + "_3d.pdf")
output_html = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + "_3d.html")
output_xlsx = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + "_3d.xlsx")
## write output files
fig.write_image(str(output_pdf))
fig.write_html(str(output_html))
ordination_result.samples[[cat1,
cat2]].to_excel(output_xlsx)
## ask to show file
answer = sg.PopupYesNo('Show plot?', keep_on_top=True)
if answer == "Yes":
webbrowser.open('file://' + str(output_html))
## print closing text
closing_text = "PCoA plots are found in: " + str(
path_to_outdirs) + "/PCoA_plots/"
sg.Popup(closing_text, title="Finished", keep_on_top=True)
## write log file
from taxontabletools.create_log import ttt_log
ttt_log("pcoa analysis", "analysis", TaXon_table_xlsx.name,
output_pdf.name, meta_data_to_test,
path_to_outdirs)
break
else:
sg.Popup("Please choose not more than 3 PCoA axes",
title="Error",
keep_on_top=True)
if event == 'Plot matrix':
if len(proportion_explained_list) >= 4:
## create a subfolder for better sorting and overview
dirName = Path(
str(path_to_outdirs) + "/" + "PCoA_plots" + "/" +
TaXon_table_xlsx.stem + "/")
if not os.path.exists(dirName):
os.mkdir(dirName)
df_pcoa = ordination_result.samples[[
"PC1", "PC2", "PC3", "PC4"
]]
df_pcoa.insert(
4, "Metadata",
Meta_data_table_df[meta_data_to_test].values.tolist(),
True)
df_pcoa.insert(
5, "Sample",
Meta_data_table_df["Samples"].values.tolist(), True)
fig = make_subplots(rows=4, cols=4)
########### 1 ###########
fig.add_trace(go.Scatter(), row=1, col=1)
fig.update_layout(template=template,
font_size=font_size,
title_font_size=font_size)
text = "PC1 (" + str(
round(
ordination_result.proportion_explained["PC1"] *
100, 2)) + " %)"
fig.add_annotation(text=text, showarrow=False)
fig.update_xaxes(showticklabels=False, showgrid=False)
fig.update_yaxes(showticklabels=False, showgrid=False)
########### 2 ###########
df = df_pcoa[["PC1", "PC2", "Metadata", "Sample"]]
for metadata in set(metadata_list):
df_metadata = df[df['Metadata'] == metadata]
#fig = px.scatter(df_pcoa, x="PC1", y="PC2", , )
fig.add_trace(go.Scatter(
x=df_metadata["PC1"].values.tolist(),
y=df_metadata["PC2"].values.tolist(),
mode='markers',
name=metadata,
text=df_metadata["Sample"].values.tolist()),
row=1,
col=2)
########### 3 ###########
df = df_pcoa[["PC1", "PC3", "Metadata", "Sample"]]
for metadata in set(metadata_list):
df_metadata = df[df['Metadata'] == metadata]
#fig = px.scatter(df_pcoa, x="PC1", y="PC2", , )
fig.add_trace(go.Scatter(
x=df_metadata["PC1"].values.tolist(),
y=df_metadata["PC3"].values.tolist(),
mode='markers',
name=metadata,
showlegend=False,
text=df_metadata["Sample"].values.tolist()),
row=1,
col=3)
########### 4 ###########
df = df_pcoa[["PC1", "PC4", "Metadata", "Sample"]]
for metadata in set(metadata_list):
df_metadata = df[df['Metadata'] == metadata]
fig.add_trace(go.Scatter(
x=df_metadata["PC1"].values.tolist(),
y=df_metadata["PC4"].values.tolist(),
mode='markers',
name=metadata,
showlegend=False,
text=df_metadata["Sample"].values.tolist()),
row=1,
col=4)
fig.update_traces(marker_size=int(pcoa_s),
mode="markers")
fig.update_xaxes(showgrid=False, row=1, col=4)
fig.update_yaxes(showgrid=False, row=1, col=4)
########### 5 ###########
fig.add_trace(go.Scatter(), row=2, col=2)
fig.update_layout(template=template,
font_size=font_size,
title_font_size=font_size)
text = "PC2 (" + str(
round(
ordination_result.proportion_explained["PC2"] *
100, 2)) + " %)"
fig.add_annotation(text=text,
showarrow=False,
row=2,
col=2)
########### 6 ###########
df = df_pcoa[["PC2", "PC3", "Metadata", "Sample"]]
for metadata in set(metadata_list):
df_metadata = df[df['Metadata'] == metadata]
#fig = px.scatter(df_pcoa, x="PC1", y="PC2", , )
fig.add_trace(go.Scatter(
x=df_metadata["PC2"].values.tolist(),
y=df_metadata["PC3"].values.tolist(),
mode='markers',
name=metadata,
showlegend=False,
text=df_metadata["Sample"].values.tolist()),
row=2,
col=3)
########### 7 ###########
df = df_pcoa[["PC2", "PC4", "Metadata", "Sample"]]
for metadata in set(metadata_list):
df_metadata = df[df['Metadata'] == metadata]
fig.add_trace(go.Scatter(
x=df_metadata["PC2"].values.tolist(),
y=df_metadata["PC4"].values.tolist(),
mode='markers',
name=metadata,
showlegend=False,
text=df_metadata["Sample"].values.tolist()),
row=2,
col=4)
########### 8 ###########
fig.add_trace(go.Scatter(), row=3, col=3)
fig.update_layout(template=template,
font_size=font_size,
title_font_size=font_size)
text = "PC3 (" + str(
round(
ordination_result.proportion_explained["PC3"] *
100, 2)) + " %)"
fig.add_annotation(text=text,
showarrow=False,
row=3,
col=3)
########### 9 ###########
df = df_pcoa[["PC3", "PC4", "Metadata", "Sample"]]
for metadata in set(metadata_list):
df_metadata = df[df['Metadata'] == metadata]
#fig = px.scatter(df_pcoa, x="PC1", y="PC2", , )
fig.add_trace(go.Scatter(
x=df_metadata["PC3"].values.tolist(),
y=df_metadata["PC4"].values.tolist(),
mode='markers',
name=metadata,
showlegend=False,
text=df_metadata["Sample"].values.tolist()),
row=3,
col=4)
########### 5 ###########
fig.add_trace(go.Scatter(), row=4, col=4)
fig.update_layout(template=template,
font_size=font_size,
title_font_size=font_size)
text = "PC4 (" + str(
round(
ordination_result.proportion_explained["PC4"] *
100, 2)) + " %)"
fig.add_annotation(text=text,
showarrow=False,
row=4,
col=4)
######################
fig.update_xaxes(showline=True,
mirror=True,
linewidth=1,
linecolor='black')
fig.update_yaxes(showline=True,
mirror=True,
linewidth=1,
linecolor='black')
fig.update_traces(marker_size=int(pcoa_s), mode="markers")
# finish plot matrix
fig.update_layout(height=1000,
width=1000,
title_text=textbox)
## define output files
output_pdf = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + "_matrix.pdf")
output_html = Path(
str(dirName) + "/" + meta_data_to_test + "_" +
taxon_title + "_matrix.html")
## write output files
fig.write_image(str(output_pdf))
fig.write_html(str(output_html))
## ask to show file
answer = sg.PopupYesNo('Show plot?', keep_on_top=True)
if answer == "Yes":
webbrowser.open('file://' + str(output_html))
## print closing text
closing_text = "\n" + "PCoA plots are found in: " + str(
path_to_outdirs) + "/PCoA_plots/"
sg.Popup(closing_text, title="Finished", keep_on_top=True)
## write to log file
from taxontabletools.create_log import ttt_log
ttt_log("pcoa analysis", "analysis", TaXon_table_xlsx.name,
output_pdf.name, meta_data_to_test,
path_to_outdirs)
break
else:
sg.Popup(
"There must be at least 4 PCoA axis available to plot the matrix!"
)
pcoa_window.close()
else:
sg.PopupError(
"The sample of both the TaXon table and the metadata table have to match!"
)
title="CoMA",
text=
"ATTENTION: At least 1 of your eigenvalues is negative, potentially leading to problems! You may want to choose another metric for distance calculation or apply data transformation on the distance matrix (e.g. square root) to get rid of this problem."
)
eig_dm = pd.DataFrame(pc.eigvals, columns=["Eigenvalue"])
eig_dm["Explained"] = pc.proportion_explained
eig_dm["Summed_explanation"] = pc.proportion_explained.cumsum()
if metric == "minkowski":
eig_dm.to_csv("eigenvalues_" + mname + "_p" + str(p) + ".txt", sep="\t")
else:
eig_dm.to_csv("eigenvalues_" + mname + ".txt", sep="\t")
#Statistics
anos = anosim(div, map_DF, column=var, permutations=999)
perm = permanova(div, map_DF, column=var, permutations=999)
if metric == "minkowski":
stat_file = "statistics_" + mname + "_p" + str(p) + "_" + var + ".txt"
else:
stat_file = "statistics_" + mname + "_" + var + ".txt"
with open(stat_file, "w") as st:
st.write("ANOSIM\tPermutations: 999\n\n")
st.write("R\t" + str(anos["test statistic"]) + "\n")
st.write("p-value\t" + str(anos["p-value"]) + "\n\n")
st.write("PERMANOVA\tPermutations: 999\n\n")
st.write("F\t" + str(perm["test statistic"]) + "\n")
st.write("p-value\t" + str(perm["p-value"]) + "\n\n")