Human eyes analysis

Edit metadata
Import experiment data
Plot PCoA for filtering
Normal QIIME2 pipeline
Maaslin
- Maaslin summarize table
- Significant results for each bacteria (with taxonomy)
Calour

library(tidyverse)
library(xlsx)
# library(rotemfuns)
library(vegan)
library(funrar)
library(subSeq)
library(qiime2R)
library(ggpubr)
library(magrittr)
library(rmarkdown)
library(gghighlight)
library(microbiomeutilities)
source("/pita/users/rotem/scripts/qiime2_funcs.R")
if(getwd() == "/pita/users/rotem/eyes/") { setwd("human") }

Edit metadata

dbmap = read.csv("/pita/pub/data/16S_DBs/maps/DB1-13_premap_v3.csv")
dbmap$reads_number = dbmap$reads_number %>% as.character() %>% as.numeric()
done = 
dbmap %>% 
  filter(Cohort == "eyes") %>% 
  # select(-Family) %>% 
  filter(Organism == "Human") %>% 
  arrange(desc(reads_number)) %>% 
  distinct(sample_ID, .keep_all = T)

prev_families = read.csv("DB3_control_families.csv")    

done =
done %>%
  full_join(dbmap %>% filter(X.SampleID %in% prev_families$X.SampleID))

done = filter(done, reads_number > 1000)
done$sample_ID = gsub("E_", "", done$sample_ID %>% as.character())
done$sample_ID = gsub("-", "_", done$sample_ID %>% as.character())
done = distinct(done, sample_ID, .keep_all = T)
done$Age %<>% as.character() %>% as.numeric()
names(done)[1] = "sampleid"
# write.table(done, "16s_map.tsv", row.names = F, sep = "\t", quote = F)

hadasmap = read.xlsx("Table for Rotem- Families 12.11.20.xlsx", 1)
hadasmap %<>% pivot_longer(cols = starts_with("DNA_Extracted_from")
                            , names_to = "Sample.type", values_to = "ID", values_drop_na = T) 
hadasmap$Sample.ID = hadasmap$ID
hadasmap$Sample.ID = str_replace_all(hadasmap$Sample.ID, "E_", replacement = "")
hadasmap$Sample.ID = str_replace_all(hadasmap$Sample.ID, "-", replacement = "_")
hadasmap$Age %<>% as.character() %>% as.numeric()
# map$Sample.ID = paste0("E_", map$Sample.ID)
hadasmap$pnid = paste0(hadasmap$Family,"-", hadasmap$Subject_.)
# Verify that all samples that already run fount Hadas table
# setdiff(done$sample_ID , map$Sample.ID)
# intersect(done$sample_ID , map$Sample.ID) %>% length() == length(done$sample_ID)

names(hadasmap)[96] = "sample_ID"
names(prev_families)[1] = "sampleid"

allmap = done %>% select(sample_ID, sampleid, reads_number, Database, Cohort) %>% 
  left_join(hadasmap %>% select(-ID), by = "sample_ID")

allmap %<>%  left_join(prev_families %>% select(sampleid, Family, Age, Gender), by = "sampleid") 
# allmap = full_join(tt
#                    , prev_families %>% select(X.SampleID, sample_ID, ID, Age, Gender, Family)
#           , by = "sample_ID") 
allmap %>% select(contains("Family"))
allmap = unite(allmap, "Family", contains("Family."), na.rm = T)
allmap = unite(allmap, "Age", contains("Age."), na.rm = T)
allmap = unite(allmap, "Gender", contains("Gender."), na.rm = T)
allmap$Gender = gsub("Male", "M", allmap$Gender)
allmap$Gender = gsub("Female", "F", allmap$Gender)
allmap$Age = allmap$Age %>% as.numeric()
allmap$Disease.group %<>% replace_na(0)
map = sample_data(allmap)
sample_names(map) = map$sampleid

# write.xlsx(hadasmap, "hadasmap_for_rotem_12Nov20.xlsx")
hadasmap %>% filter(Family == "FR5") %>% select(ID)
map %>% data.frame() %>% filter(is.na(Sample.type))

Import experiment data

biompath = "/pita/pub/data/16S_DBs/processed/16S_DB1-13_merged/table.qza"
taxpath = "/pita/pub/data/16S_DBs/processed/16S_DB1-13_merged/taxonomy.qza"
biom = read_qza(biompath)$data
biom = biom %>% data.frame() 
biom = biom %>% select(sample_names(map))
min_reads_cutoff = 2000
biom = biom[apply(biom, 2, sum) > min_reads_cutoff]
rare_biom = vegan::rrarefy(biom %>% t(), 10000) %>% t()
otu = phyloseq::otu_table(rare_biom, taxa_are_rows = T)
otu = phyloseq::prune_taxa(taxa_sums(otu) > 10, otu)
relative_otu = otu_table(funrar::make_relative(otu@.Data), taxa_are_rows = T)
relative_otu = filter_taxa(relative_otu,  function(x) mean(x) > 1e-5, TRUE)

pretax = read_qza(taxpath)$data
tax = pretax %>% phyloseq::tax_table()
phyloseq::taxa_names(tax) = pretax$Feature.ID

tree = ape::rtree(ntaxa(tax), rooted = T, tip.label = phyloseq::taxa_names(tax))


abs_exp = phyloseq(otu, map, tax, tree)
exp = phyloseq(relative_otu, map, tax, tree)

Plot PCoA for filtering

ord <- ordinate(exp, method = "PCoA", distance = "unifrac")
plot_ordination(exp, ord, color = "Sample.type") + theme_q2r()

### Filter feaces data

exp_feaces = exp@sam_data %>% data.frame() %>% 
  filter(Sample.type == "DNA_Extracted_from_feaces...F." | is.na(Sample.type)) %>% 
    rownames() %>%  prune_samples(exp)

exp_feaces %>% sample_data() %>% data.frame() %>% 
  select(c(2,1,3:length(exp@sam_data %>% data.frame()))) %>% 
  write.table(file = "human_feaces.tsv", sep = "\t", quote = F, row.names = F, col.names = T)

exp_mouth = exp@sam_data %>% data.frame() %>% 
  filter(!(Sample.type == "DNA_Extracted_from_feaces...F." | is.na(Sample.type))) %>%
  rownames() %>% prune_samples(exp)

exp_mouth %>% sample_data() %>% data.frame() %>% 
  select(c(2,1,3:length(exp@sam_data %>% data.frame()))) %>% 
  write.table(file = "human_mouth.tsv", sep = "\t", quote = F, row.names = F, col.names = T)

Normal QIIME2 pipeline

/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime feature-table filter-samples \
      --i-table /pita/pub/data/16S_DBs/processed/16S_DB1-13_merged/table.qza \
      --m-metadata-file /pita/users/rotem/eyes/human/human_feaces.tsv \
      --o-filtered-table /pita/users/rotem/eyes/human/human_feaces_table.qza

system(paste0(qiime_path, "feature-table filter-samples ",
               "--i-table /pita/pub/data/16S_DBs/processed/16S_DB1-13_merged/table.qza ", 
               "--m-metadata-file human_mouth.tsv ", 
               "--o-filtered-table mouth/human_mouth.qza ") )

#/pita/users/rotem/bin/save_rep_seq.sh human_feaces_table.qza

save_rep_seq("human_feaces_table.qza")
save_rep_seq("mouth/human_mouth.qza", "mouth/rep-seqs.qza")

this chunk not working in notebook, run seperatly in qiime2 environment

# alias run-sepp.sh='/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/run-sepp.sh'

/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime fragment-insertion sepp \
      --i-representative-sequences rep-seqs.qza \
      --i-reference-database /pita/pub/gg_13_8_otus/sepp-refs-gg-13-8.qza \
      --o-tree rooted-tree.qza \
      --o-placements insertion-placements.qza \
      --p-threads 42

mkdir res
rm -r res/core-metrics-results
/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime diversity core-metrics-phylogenetic \
      --i-phylogeny rooted-tree.qza \
      --i-table human_feaces_table.qza \
      --p-sampling-depth 2000 \
      --m-metadata-file human_feaces.tsv \
      --output-dir ./res/core-metrics-results \
      --p-n-jobs 20

/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime diversity alpha-group-significance \
          --i-alpha-diversity res/core-metrics-results/faith_pd_vector.qza \
          --m-metadata-file human_feaces.tsv \
          --o-visualization res/core-metrics-results/faith-pd-group-significance.qzv

/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime diversity alpha-correlation \
          --i-alpha-diversity res/core-metrics-results/faith_pd_vector.qza \
          --m-metadata-file human_feaces.tsv \
          --o-visualization res/core-metrics-results/faith-pd-correlation.qzv

# /pita/pub/scripts/qiime2/gg_99_classifier_150bp.qza
/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime feature-classifier classify-sklearn \
     --i-classifier training-feature-classifiers/classifier.qza  \
     --i-reads rep-seqs.qza \
     --o-classification res/taxonomy.qza \
     --p-n-jobs 20
     
/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime metadata tabulate \
     --m-input-file res/taxonomy.qza  \
     --o-visualization res/taxonomy.qzv

/pita/users/rotem/miniconda3/envs/qiime2-2020.2/bin/qiime taxa barplot \
     --i-table human_feaces_table.qza \
     --i-taxonomy res/taxonomy.qza \
   --m-metadata-file human_feaces.tsv \
     --o-visualization res/taxa-bar-plots.qzv

Fecal Beta diversity color by family and disease status

# exp@sam_data %>% data.frame() %>% filter(is.na(Sample.type))
ord_f <- ordinate(exp_feaces, method = "PCoA", distance = "unifrac")

pl = ord_f$vectors %>% data.frame() %>% rownames_to_column("index") %>% 
  left_join(exp_feaces@sam_data %>% data.frame() %>% rownames_to_column("index")) %>% 
  ggplot(aes(x = Axis.1, y = Axis.2, color = Family,  shape = Disease.group)) + 
  geom_point() +
  gghighlight(label_key = Family) +
  facet_wrap(~Family) +
   theme_q2r()
plotly::ggplotly(pl, width = 800)

# pl

pl = ord_f$vectors %>% data.frame() %>% rownames_to_column("index") %>% 
  left_join(exp_feaces@sam_data %>% data.frame() %>% rownames_to_column("index")) %>% 
  ggplot(aes(x = Axis.1, y = Axis.2, shape = Opthalmology, color = Family)) + 
  geom_point() +
  gghighlight(label_key = Family) +
  facet_wrap(~Family) +
   theme_q2r()
plotly::ggplotly(pl ,  width = 800)

# pl

Mouth Beta diversity color by family and disease status

ord_s <- ordinate(exp_mouth, method = "PCoA", distance = "unifrac")

pl = ord_s$vectors %>% data.frame() %>% rownames_to_column("index") %>% 
  left_join(exp_mouth@sam_data %>% data.frame() %>% rownames_to_column("index")) %>% 
  ggplot(aes(x = Axis.1, y = Axis.2,  color = Family, shape = Disease.group)) +
  geom_point(size = 2) +
  gghighlight(label_key = Family) +
  facet_wrap(~Family) +
 theme_q2r()
plotly::ggplotly(pl ,  width = 800)

# pl

Alpha diversity for each family and disease status

faith = read_qza("res/core-metrics-results/faith_pd_vector.qza")$data

pl = 
faith %>% rownames_to_column("index") %>% 
  left_join(exp_feaces@sam_data %>% data.frame() %>% rownames_to_column("index")) %>% 
  group_by(Family) %>% filter(length(pnid) > 2) %>% 
  ggplot(aes(x = sample_ID, y = faith_pd, color = Disease.group)) + 
  geom_point() + 
  # geom_hline(aes(yintercept = mean(faith_pd)), color = "red", alpha = .25) +
  facet_wrap(~Family, scales = "free_x") +
  theme_q2r()
plotly::ggplotly(pl)

Maaslin

For: Family, Disease.group, Age, Weight_.gr, BMI, and Gender.

short_bacteria_name <- function(Taxon) {
  split_taxonomy = strsplit(as.character(Taxon), split = "[kpcofgs]__")[[1]]
  short_taxonomy = split_taxonomy[nchar(split_taxonomy) > 4]
  # remove bacteria prefix
  paste(short_taxonomy[2:length(short_taxonomy)], collapse = " ")
}

Maaslin summarize table

Number of significant results for each category

maaslin_sig_results <- read.csv("maaslin/significant_results.tsv", sep = "\t")
fulltaxonomy = read_qza("res/taxonomy.qza")$data
fulltaxonomy$short = sapply(fulltaxonomy$Taxon, short_bacteria_name)
names(fulltaxonomy)[1] = "feature"

maaslin_sig_results %>% group_by(metadata, value) %>% 
  summarise(significant.SVs = length(feature)) %>% paged_table()

Significant results for each bacteria (with taxonomy)

pl = 
maaslin_sig_results %>% left_join(fulltaxonomy, by = "feature") %>% 
  select(metadata, value, Taxon, coef, qval) %>% 
  top_n(60, qval) %>%
  ggplot(aes(x = metadata, y = Taxon, fill = qval*sign(coef))) + 
  geom_tile() + 
  scale_fill_gradientn(colors = c('orange', 'red', 'black', 'blue'))+
  theme_q2r()  + 
  theme(axis.text.x = element_text(angle = 270), axis.text.y = element_text(size = 12))
  # theme(axis.text.y = element_blank(), axis.ticks.y = element_blank())
# plotly::ggplotly(pl)
  # DT::datatable(style = "bootstrap")
  # paged_table()
pl

# SV <- maaslin_sig_results %>% filter(metadata == "Disease.group") %>% 
#   top_n(1, qval) %>% pull(feature)
# 
# exp_feaces %>% otu_table() %>% data.frame() %>% t() %>% data.frame() %>% 
#   select(bac = SV[2]) %>% 
#   rownames_to_column("index") %>% 
#   left_join(exp_feaces@sam_data %>% data.frame() %>% rownames_to_column("index")) %>% 
#   ggplot(aes(x = Disease.group, color = Disease.group, y = log(bac))) + 
#   geom_boxplot() + 
#   geom_jitter(alpha = .5) +
#   ggtitle(SV[2]) 
#   theme_q2r()

Calour

# allmap$Cohort %>% factor() %>% levels()
ff = exp %>% sample_data() %>% pull(Cohort) %>% factor() %>% levels()

import calour as ca
import pandas as pd
import os
import sys
import matplotlib  
import numpy as np 
import calour_utils as cu

## failed to load logging config file

matplotlib.use("Agg", force = True)

exp = ca.read_qiime2(r.biompath, "human_feaces.tsv", min_reads=2000, normalize=10000)

## 2020-12-06 15:30:49 WARNING These have data but do not have metadata: {'DB5.123', 'DB1.114', 'DB4.133', 'DB5.153', 'DB5.163', 'DB10.058', 'DB2.083', 'DB12.168', 'DB12.262', 'DB6.115', 'DB5.242', 'DB12.320', 'DB2.201', 'DB9.294', 'DB10.119', 'DB10.262', 'DB12.017', 'DB10.208', 'DB1.096', 'DB5.080', 'DB1.223', 'DB13.207', 'DB11.192', 'DB12.055', 'DB13.212', 'DB9.233', 'DB4.131', 'DB8.089', 'DB3.019', 'DB6.178', 'DB12.305', 'DB12.050', 'DB5.083', 'DB2.128', 'DB4.126', 'DB11.176', 'DB1.000', 'DB5.159', 'DB11.207', 'DB6.257', 'DB4.063', 'DB10.189', 'DB3.125', 'DB6.025', 'DB8.156', 'DB9.221', 'DB13.266', 'DB3.047', 'DB6.134', 'DB7.106', 'DB4.033', 'DB11.007', 'DB12.166', 'DB5.238', 'DB6.164', 'DB11.043', 'DB12.159', 'DB11.280', 'DB4.205', 'DB5.216', 'DB3.067', 'DB10.145', 'DB11.328', 'DB3.168', 'DB2.193', 'DB1.176', 'DB2.195', 'DB11.000', 'DB8.023', 'DB11.323', 'DB10.234', 'DB13.239', 'DB13.096', 'DB12.093', 'DB6.208', 'DB12.299', 'DB7.013', 'DB5.292', 'DB10.146', 'DB13.140', 'DB10.167', 'DB13.071', 'DB7.074', 'DB4.165', 'DB9.066', 'DB10.065', 'DB9.182', 'DB13.120', 'DB3.012', 'DB7.217', 'DB1.148', 'DB3.251', 'DB8.198', 'DB6.015', 'DB9.002', 'DB3.046', 'DB2.085', 'DB4.090', 'DB13.285', 'DB12.321', 'DB13.134', 'DB13.069', 'DB7.214', 'DB6.065', 'DB10.089', 'DB11.077', 'DB6.113', 'DB11.287', 'DB2.221', 'DB8.147', 'DB9.223', 'DB1.272', 'DB10.256', 'DB10.152', 'DB9.038', 'DB5.147', 'DB10.209', 'DB4.087', 'DB11.239', 'DB13.233', 'DB12.212', 'DB13.222', 'DB12.281', 'DB7.165', 'DB3.136', 'DB4.250', 'DB11.304', 'DB8.200', 'DB6.125', 'DB12.352', 'DB11.016', 'DB13.250', 'DB6.062', 'DB11.297', 'DB5.237', 'DB3.042', 'DB3.095', 'DB10.232', 'DB13.054', 'DB1.018', 'DB12.102', 'DB13.127', 'DB5.167', 'DB10.183', 'DB5.118', 'DB1.031', 'DB3.204', 'DB6.078', 'DB1.102', 'DB8.124', 'DB12.226', 'DB7.243', 'DB7.122', 'DB10.036', 'DB2.190', 'DB13.315', 'DB13.084', 'DB10.169', 'DB5.249', 'DB4.081', 'DB11.143', 'DB7.258', 'DB7.069', 'DB11.180', 'DB2.135', 'DB2.077', 'DB7.280', 'DB11.228', 'DB1.151', 'DB1.215', 'DB12.348', 'DB1.097', 'DB10.153', 'DB13.205', 'DB3.043', 'DB8.157', 'DB7.116', 'DB10.223', 'DB12.266', 'DB9.297', 'DB3.023', 'DB10.017', 'DB9.019', 'DB8.280', 'DB9.077', 'DB1.243', 'DB8.173', 'DB11.243', 'DB7.278', 'DB5.285', 'DB8.283', 'DB2.194', 'DB8.202', 'DB9.194', 'DB7.061', 'DB1.022', 'DB2.132', 'DB1.042', 'DB11.102', 'DB4.032', 'DB12.286', 'DB2.209', 'DB3.027', 'DB4.220', 'DB3.132', 'DB12.141', 'DB13.209', 'DB6.211', 'DB4.026', 'DB4.249', 'DB1.191', 'DB2.226', 'DB4.145', 'DB5.093', 'DB8.020', 'DB7.134', 'DB12.240', 'DB6.162', 'DB1.130', 'DB9.020', 'DB1.249', 'DB5.139', 'DB1.010', 'DB11.253', 'DB3.266', 'DB11.318', 'DB12.132', 'DB13.304', 'DB8.030', 'DB3.118', 'DB5.099', 'DB4.089', 'DB10.101', 'DB13.075', 'DB12.346', 'DB11.251', 'DB8.258', 'DB9.114', 'DB11.104', 'DB4.095', 'DB6.244', 'DB3.001', 'DB6.024', 'DB7.152', 'DB12.154', 'DB4.132', 'DB7.144', 'DB9.225', 'DB13.337', 'DB13.185', 'DB3.158', 'DB4.241', 'DB1.122', 'DB1.132', 'DB5.145', 'DB10.077', 'DB4.129', 'DB5.065', 'DB10.042', 'DB11.184', 'DB7.259', 'DB11.151', 'DB8.268', 'DB10.043', 'DB6.104', 'DB12.229', 'DB8.271', 'DB3.086', 'DB4.190', 'DB6.201', 'DB10.125', 'DB13.095', 'DB12.090', 'DB2.103', 'DB4.016', 'DB7.090', 'DB11.047', 'DB12.155', 'DB3.201', 'DB13.357', 'DB12.063', 'DB12.247', 'DB6.161', 'DB7.096', 'DB10.274', 'DB4.260', 'DB3.044', 'DB6.137', 'DB3.243', 'DB4.039', 'DB10.160', 'DB6.268', 'DB12.041', 'DB5.257', 'DB13.269', 'DB5.196', 'DB7.163', 'DB2.076', 'DB11.052', 'DB9.078', 'DB1.141', 'DB12.014', 'DB13.261', 'DB1.045', 'DB12.101', 'DB4.275', 'DB12.300', 'DB12.146', 'DB10.002', 'DB4.299', 'DB7.062', 'DB1.021', 'DB6.262', 'DB8.143', 'DB4.034', 'DB5.279', 'DB6.253', 'DB7.201', 'DB10.296', 'DB1.231', 'DB9.156', 'DB10.210', 'DB1.101', 'DB7.234', 'DB3.092', 'DB11.031', 'DB1.086', 'DB7.059', 'DB4.213', 'DB13.044', 'DB8.092', 'DB10.154', 'DB10.201', 'DB13.146', 'DB5.184', 'DB5.269', 'DB9.169', 'DB5.056', 'DB9.096', 'DB9.119', 'DB10.131', 'DB11.003', 'DB11.295', 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'DB9.084', 'DB1.267', 'DB3.210', 'DB12.233', 'DB11.128', 'DB11.039', 'DB10.074', 'DB2.213', 'DB9.208', 'DB4.266', 'DB12.062', 'DB2.101', 'DB11.108', 'DB2.136', 'DB4.201', 'DB10.188', 'DB8.076', 'DB11.002', 'DB12.288', 'DB8.053', 'DB4.009', 'DB4.225', 'DB5.212', 'DB9.047', 'DB6.205', 'DB10.229', 'DB5.207', 'DB13.240', 'DB7.083', 'DB13.109', 'DB13.359', 'DB4.141', 'DB3.007', 'DB10.148', 'DB11.146', 'DB8.246', 'DB9.266', 'DB13.147', 'DB11.338', 'DB11.115', 'DB7.170', 'DB1.120', 'DB5.046', 'DB11.291', 'DB13.338', 'DB2.219', 'DB7.260', 'DB10.247', 'DB12.048', 'DB13.191', 'DB9.196', 'DB6.034', 'DB13.173', 'DB10.239', 'DB12.137', 'DB2.244', 'DB12.303', 'DB10.248', 'DB11.185', 'DB1.227', 'DB7.025', 'DB7.129', 'DB1.044', 'DB10.241', 'DB1.069', 'DB12.068', 'DB6.022', 'DB4.245', 'DB11.191', 'DB11.033', 'DB12.310', 'DB10.255', 'DB13.196', 'DB2.134', 'DB9.023', 'DB1.061', 'DB5.136', 'DB7.039', 'DB7.009', 'DB11.313', 'DB4.001', 'DB1.168', 'DB7.196', 'DB4.124', 'DB1.187', 'DB6.169', 'DB11.046', 'DB9.018', 'DB11.320', 'DB11.129', 'DB9.251', 'DB4.028', 'DB10.211', 'DB12.046', 'DB8.083', 'DB10.195', 'DB12.270', 'DB5.062', 'DB1.080', 'DB9.260', 'DB2.037', 'DB13.111', 'DB8.006', 'DB6.175', 'DB12.131', 'DB8.179', 'DB12.306', 'DB12.108', 'DB12.280', 'DB10.026', 'DB9.299', 'DB4.121', 'DB1.029', 'DB13.213', 'DB9.083', 'DB10.173', 'DB3.184', 'DB1.251', 'DB6.242', 'DB13.298', 'DB11.087', 'DB8.163', 'DB11.290', 'DB2.142', 'DB3.241', 'DB1.233', 'DB2.147', 'DB10.094', 'DB3.045', 'DB11.056', 'DB10.108', 'DB3.268', 'DB13.164', 'DB4.226', 'DB10.179', 'DB6.009', 'DB4.210', 'DB9.022', 'DB13.311', 'DB5.017', 'DB8.151', 'DB7.169', 'DB8.155', 'DB5.037', 'DB8.117', 'DB12.312', 'DB7.127', 'DB6.082', 'DB4.243', 'DB12.278', 'DB10.050', 'DB1.023', 'DB13.068', 'DB11.124', 'DB4.138', 'DB6.266', 'DB10.194', 'DB1.139', 'DB2.176', 'DB4.208', 'DB8.185', 'DB12.075', 'DB5.280', 'DB10.290', 'DB5.175', 'DB7.294', 'DB13.136', 'DB13.154', 'DB4.195', 'DB6.088', 'DB10.197', 'DB2.030', 'DB11.261', 'DB3.068', 'DB6.174', 'DB12.009', 'DB1.111', 'DB3.208', 'DB7.100', 'DB1.264', 'DB6.002', 'DB12.268', 'DB5.157', 'DB4.169', 'DB11.238', 'DB12.248', 'DB10.231', 'DB4.123', 'DB10.047', 'DB9.071', 'DB8.183', 'DB12.054', 'DB7.093', 'DB6.218', 'DB5.158', 'DB11.194', 'DB2.032', 'DB12.099', 'DB4.144', 'DB12.109', 'DB4.154', 'DB9.190', 'DB12.333', 'DB1.074', 'DB9.206', 'DB7.216', 'DB10.273', 'DB13.203', 'DB11.080', 'DB13.319', 'DB10.267', 'DB13.105', 'DB9.115', 'DB9.295', 'DB11.024', 'DB11.285', 'DB4.212', 'DB3.090', 'DB13.174', 'DB8.284', 'DB1.073', 'DB3.131', 'DB10.012', 'DB12.083', 'DB8.270', 'DB1.274', 'DB12.139', 'DB6.185', 'DB4.223', 'DB12.345', 'DB13.143', 'DB7.211', 'DB6.222', 'DB5.133', 'DB2.187', 'DB5.094', 'DB4.274', 'DB3.024', 'DB9.067', 'DB13.181', 'DB1.124', 'DB4.099', 'DB5.251', 'DB7.179', 'DB7.254', 'DB8.064', 'DB8.034', 'DB12.296', 'DB13.265', 'DB12.169', 'DB7.187', 'DB3.038', 'DB2.166', 'DB6.138', 'DB7.269', 'DB8.278', 'DB12.297', 'DB9.212', 'DB12.069', 'DB3.018', 'DB5.061', 'DB13.259', 'DB12.259', 'DB5.186', 'DB13.214', 'DB3.267', 'DB7.232', 'DB9.088', 'DB10.281', 'DB1.269', 'DB8.267', 'DB5.203', 'DB11.123', 'DB9.068', 'DB12.081', 'DB6.179', 'DB2.220', 'DB6.173', 'DB7.080', 'DB4.149', 'DB6.231', 'DB6.212', 'DB3.150', 'DB9.275', 'DB11.001', 'DB10.261', 'DB12.058', 'DB13.097', 'DB7.279', 'DB11.224', 'DB6.038', 'DB12.250', 'DB2.058', 'DB4.134', 'DB7.252', 'DB6.293', 'DB13.086', 'DB5.256', 'DB9.034', 'DB12.230', 'DB7.140', 'DB9.124', 'DB11.284', 'DB13.063', 'DB10.286', 'DB2.130', 'DB3.117', 'DB7.160', 'DB3.172', 'DB1.298', 'DB5.067', 'DB4.143', 'DB1.286', 'DB9.292', 'DB10.122', 'DB13.279', 'DB8.118', 'DB8.232', 'DB10.142', 'DB5.168', 'DB2.234', 'DB7.112', 'DB6.168', 'DB13.172', 'DB1.088', 'DB4.097', 'DB8.025', 'DB1.228', 'DB12.125', 'DB4.055', 'DB1.155', 'DB13.195', 'DB3.036', 'DB8.137', 'DB10.083', 'DB11.045', 'DB7.197', 'DB10.282', 'DB6.215', 'DB9.195', 'DB4.232', 'DB4.067', 'DB11.236', 'DB2.141', 'DB7.283', 'DB13.099', 'DB10.291', 'DB2.112', 'DB8.054', 'DB8.123', 'DB8.287', 'DB2.073', 'DB12.053', 'DB10.135', 'DB8.190', 'DB5.132', 'DB9.172', 'DB11.264', 'DB8.242', 'DB2.087', 'DB11.254', 'DB4.085', 'DB4.214', 'DB3.155', 'DB2.067', 'DB7.248', 'DB11.088', 'DB4.060', 'DB6.176', 'DB10.022', 'DB3.057', 'DB7.038', 'DB12.013', 'DB13.040', 'DB4.238', 'DB1.003', 'DB2.007', 'DB3.129', 'DB7.193', 'DB12.077', 'DB12.227', 'DB7.233', 'DB13.305', 'DB8.241', 'DB13.322', 'DB7.065', 'DB7.124', 'DB10.283', 'DB8.298', 'DB1.179', 'DB2.115', 'DB6.071', 'DB8.228', 'DB11.332', 'DB10.263', 'DB13.330', 'DB7.004', 'DB6.259', 'DB1.142', 'DB6.017', 'DB6.029', 'DB10.070', 'DB12.232', 'DB7.218', 'DB12.276', 'DB13.141', 'DB8.095', 'DB2.114', 'DB13.194', 'DB9.220', 'DB4.112', 'DB13.229', 'DB12.285', 'DB9.207', 'DB9.102', 'DB2.092', 'DB5.211', 'DB7.015', 'DB7.072', 'DB13.188', 'DB5.213', 'DB5.253', 'DB1.026', 'DB2.097', 'DB6.299', 'DB1.189', 'DB4.157', 'DB1.222', 'DB11.310', 'DB10.250', 'DB7.167', 'DB5.034', 'DB12.318', 'DB1.209', 'DB12.319', 'DB11.199', 'DB11.190', 'DB7.118', 'DB2.003', 'DB7.200', 'DB11.100', 'DB6.187', 'DB6.119', 'DB9.100', 'DB1.001', 'DB4.152', 'DB5.073', 'DB3.187', 'DB2.053', 'DB5.002', 'DB8.286', 'DB1.268', 'DB6.281', 'DB12.323', 'DB1.237', 'DB12.311', 'DB2.241', 'DB5.042', 'DB3.188', 'DB3.264', 'DB11.044', 'DB1.136', 'DB4.219', 'DB2.105', 'DB5.191', 'DB5.177', 'DB10.172', 'DB2.227', 'DB6.195', 'DB3.122', 'DB9.025', 'DB9.065', 'DB10.163', 'DB1.116', 'DB4.196', 'DB1.055', 'DB6.196', 'DB13.281', 'DB7.145', 'DB11.015', 'DB13.113', 'DB13.252', 'DB6.106', 'DB7.037', 'DB1.108', 'DB13.171', 'DB6.282', 'DB11.213', 'DB6.047', 'DB2.052', 'DB8.011', 'DB2.154', 'DB6.003', 'DB12.134', 'DB4.120', 'DB6.283', 'DB5.259', 'DB10.041', 'DB4.018', 'DB8.174', 'DB2.024', 'DB8.144', 'DB4.161', 'DB1.170', 'DB12.216', 'DB4.040', 'DB9.131', 'DB7.139', 'DB5.278', 'DB8.199', 'DB7.047', 'DB5.144', 'DB12.260', 'DB3.167', 'DB2.078', 'DB3.004', 'DB8.002', 'DB2.206', 'DB5.112', 'DB7.267', 'DB9.008', 'DB10.147', 'DB13.343', 'DB11.219', 'DB8.248', 'DB8.122', 'DB11.273', 'DB13.271', 'DB3.157', 'DB11.094', 'DB1.221', 'DB4.269', 'DB1.242', 'DB9.210', 'DB8.192', 'DB11.275', 'DB1.175', 'DB5.246', 'DB11.175', 'DB5.190', 'DB8.031', 'DB9.142', 'DB6.099', 'DB9.187', 'DB5.101', 'DB1.147', 'DB6.224', 'DB10.227', 'DB11.235', 'DB1.007', 'DB7.237', 'DB8.008', 'DB9.007', 'DB9.134', 'DB12.347', 'DB1.186', 'DB1.207', 'DB8.273', 'DB8.022', 'DB10.176', 'DB13.339', 'DB10.280', 'DB6.048', 'DB7.082', 'DB7.077', 'DB8.150', 'DB5.264', 'DB7.174', 'DB8.256', 'DB1.051', 'DB6.110', 'DB8.177', 'DB1.254', 'DB10.257', 'DB10.045', 'DB11.308', 'DB5.115', 'DB6.111', 'DB11.278', 'DB12.329', 'DB1.033', 'DB11.004', 'DB11.137', 'DB12.255', 'DB6.057', 'DB7.041', 'DB3.078', 'DB5.082', 'DB12.242', 'DB10.028', 'DB5.226', 'DB13.104', 'DB1.288', 'DB6.251', 'DB13.189', 'DB2.138', 'DB6.263', 'DB12.021', 'DB9.000', 'DB11.082', 'DB10.128', 'DB3.263', 'DB11.158', 'DB2.025', 'DB8.070', 'DB12.234', 'DB12.049', 'DB2.102', 'DB10.190', 'DB5.092', 'DB12.182', 'DB6.103', 'DB10.069', 'DB11.090', 'DB5.078', 'DB6.265', 'DB11.065', 'DB8.245', 'DB4.148', 'DB4.023', 'DB8.196', 'DB12.277', 'DB10.111', 'DB13.158', 'DB2.089', 'DB12.349', 'DB4.252', 'DB13.128', 'DB1.047', 'DB8.111', 'DB9.017', 'DB7.078', 'DB2.146', 'DB6.188', 'DB3.009', 'DB6.008', 'DB8.075', 'DB9.291', 'DB3.147', 'DB13.282', 'DB7.288', 'DB11.064', 'DB11.125', 'DB10.149', 'DB1.066', 'DB1.005', 'DB3.033', 'DB11.101', 'DB1.174', 'DB10.168', 'DB13.101', 'DB6.298', 'DB11.335', 'DB12.124', 'DB10.082', 'DB4.107', 'DB12.074', 'DB5.218', 'DB9.090', 'DB3.134', 'DB4.171', 'DB8.210', 'DB7.049', 'DB9.118', 'DB7.022', 'DB2.120', 'DB11.163', 'DB7.180', 'DB5.215', 'DB3.088', 'DB4.086', 'DB8.097', 'DB9.069', 'DB2.044', 'DB3.174', 'DB6.170', 'DB4.052', 'DB5.250', 'DB5.026', 'DB7.024', 'DB7.130', 'DB5.024', 'DB11.029', 'DB8.105', 'DB11.023', 'DB11.234', 'DB9.091', 'DB13.340', 'DB4.140', 'DB6.060', 'DB5.282', 'DB1.217', 'DB11.105', 'DB7.290', 'DB11.109', 'DB9.163', 'DB3.213', 'DB7.276', 'DB9.104', 'DB8.082', 'DB10.193', 'DB13.178', 'DB12.308', 'DB12.174', 'DB7.249', 'DB2.088', 'DB13.349', 'DB9.177', 'DB6.087', 'DB7.275', 'DB1.059', 'DB8.274', 'DB11.258', 'DB13.108', 'DB7.213', 'DB8.005', 'DB13.006', 'DB4.077', 'DB13.295', 'DB11.215', 'DB2.211', 'DB6.030', 'DB9.270', 'DB12.224', 'DB8.285', 'DB8.110', 'DB6.105'}
## 2020-12-06 15:30:50 WARNING Do you forget to normalize your data? It is required before running this function

exp = exp.filter_by_metadata("Cohort",r.ff)
expc = exp.cluster_features(10)

expc = expc.sort_by_metadata("Family")
expc = expc.sort_by_metadata("pnid")
expc.plot(gui = "cli", barx_fields = ["Family", "Disease.group"])

## <calour.heatmap.plotgui_cli.PlotGUI_CLI object at 0x7f5a22340080>
## 
## /home/rstudio/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/calour/heatmap/heatmap.py:309: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. In future versions, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = copy.copy(mpl.cm.get_cmap("viridis"))
##   cmap.set_bad(bad_color)

for family in filter(lambda v: v==v, expc.sample_metadata["Family"].unique()):
    # print(family)
    plot_family = expc.filter_by_metadata("Family", [family])
    pl = plot_family.plot(gui = "cli", barx_fields = ["pnid", "Disease.group", "Family"])
    pl.resize_figure(12,8)
    pl.figure.tight_layout()
    pl.save_figure("./heatmaps/" +family + '.png')

## /home/rstudio/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/calour/heatmap/plotgui.py:117: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
##   self.figure = plt.figure()