R Notebook
untill here all the analysis done on the server according to Tzippy pipeline based on “Moving Pictures” tutorial from QIIME2 site. The pipeline run with run-analysys.sh at 172.35.15.214/pita/users/rotem/firsttry. The data copied and unzipped to the data directory.
#' extract qiime files by a specific file extension
#'
#' @param filepath Path of the qza/v file to be extract
#' @param extension File extension to unzipped from filepath. format \code{'*csv'}.
#' @return Extracted files with \code{extension} in the qiime \code{filepath} file.
#' @examples
#' extract.qiime("path\to\qiime\file\qzv", extension = "*csv")
extract.qiime = function (filepath, extension) {
library(tools)
library(filesstrings)
shortname = file_path_sans_ext(filepath)
tozip = unzip(filepath, exdir = "data", list = T)[,1]
unzip( filepath
, exdir = "data/"
, files = tozip[grepl(pattern = extension, tozip)]
, overwrite = T)
dir.create(shortname)
unzipped = list.files( pattern = extension
, full.names = T
, recursive = T
, include.dirs = T)
file.move(files = unzipped, destinations = shortname, overwrite = TRUE)
system("find . -type d -empty -delete")
}
#' Return last taxa names for specific SV
#'
#' @param SV The requested SV.
#' @param taxonomy_table taxonomy table with KPCOFGS order, with confidence level at last column and SV at the first, taxa levels unknown should be empty.
#' @param depth Optional, the depth of the taxonomical level to be returned.
#' @return The last [depth=2] non empty taxonomical values for specific SV expect of specie level.
#' @examples
#' get.last.name(SV = "AAAAAAAAAA", taxonomy_table, depth =2)
get.last.name = function(SV, taxonomy_table, depth = 2) {
taxonomy_levels = c("kingdom", "phylum","class","order"
,"family", "genus", "specie")
taxonomy_table = taxonomy_table[,c("feature", taxonomy_levels)]
specific = taxonomy_table[taxonomy_table[,1] == SV,]
specific = specific[specific != ""]
specific = specific[specific != " "]
specific = specific[!is.na(specific)]
specific = specific[nchar(specific) > 4]
rev(specific)[1:depth]
paste(rev(rev(specific)[1:depth]), collapse = " ")
}
library(tidyverse)
extract.qiime(filepath = "data/taxa-bar-plots.qzv", extension = "*csv")'data/taxa-bar-plots' already exists7 files moved. 7 failed.extract.qiime("data/taxa-bar-plots.qzv", "*tsv")'data/taxa-bar-plots' already exists4 files moved. 0 failed.source("../scripts/add_sig_asterix_boxplot_funcs.R")
level2 = read.csv("data/taxa-bar-plots/level-2.csv")
datm = level2 %>%
select(index, Type, Diet, Antibiotics, contains("k__"))# %>%
#gather(Group, value = number,-index, -Type, -Diet, -Antibiotics) %>%
#group_by(index, Group) %>% summarize(RA = number / sum(number))
#group_by(Type, Group) %>% summarise(median = median(value)) %>%
RAtable = datm %>% select(index, Type, Diet, Antibiotics) %>%
bind_cols(prop.table(datm %>%
select(contains("k__")) %>%
as.matrix(), 1) %>%
as.data.frame()) %>%
gather(Group, value = frequency, -index, -Type, -Diet, -Antibiotics)
#RAtable$Type = factor(medianRA$Type, levels =c("Control", "Fructose", "Control_AB", "Fructose_AB") )
medianRA = RAtable %>%
group_by(Type, Group) %>%
summarise(median = median(frequency))
medianRA$Type = factor(medianRA$Type, levels =c("Control", "Fructose", "Control_AB", "Fructose_AB") )
ggplot() +
geom_bar(data = medianRA
, aes(y = median, x = Type, fill = Group)
, stat = "identity", position="fill") +
theme_classic()
levels(RAtable$Antibiotics) = c("No Antibiotics", "Antibiotics")
proteo = RAtable %>% filter(Group == "k__Bacteria.p__Proteobacteria")source("../scripts/asteriks.R")
pp = ggplot() +
geom_boxplot(data = proteo, aes(x = Antibiotics, fill = Diet, y = frequency))
boxplot_2_vars_grouped_add_asterix(pp, proteo, "Antibiotics", "frequency", "Diet"
, test = "wilcox", fdr = 1) +
theme_classic() +
ylab("Proteobacteria RA") +
xlab("")
proteo_noAB = proteo %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() +
geom_boxplot(data = proteo_noAB, aes(x = Diet, fill = Diet, y = frequency))
pp = add_significant_asterix_to_plot_bothType(pp
, proteo_noAB$Diet
, proteo_noAB$frequency
, test_type = "wilcox")
pp[[1]] +
theme_classic() +
ylab("Proteobacteria RA") +
xlab("")
Bacteroidetes = RAtable %>% filter(Group == "k__Bacteria.p__Bacteroidetes")
pp = ggplot() +
geom_boxplot(data = Bacteroidetes, aes( x = Antibiotics
, fill = Diet
, y = frequency))
boxplot_2_vars_grouped_add_asterix(pp, Bacteroidetes, "Antibiotics"
, "frequency", "Diet" , test = "wilcox", fdr = 1) +
theme_classic() +
ylab("Bacteroidetes RA") +
xlab("")
Bacteroidetes_noAB = Bacteroidetes %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() +
geom_boxplot(data = Bacteroidetes_noAB, aes(x = Diet, fill = Diet, y = frequency))
pp = add_significant_asterix_to_plot_bothType(pp
, Bacteroidetes_noAB$Diet
, Bacteroidetes_noAB$frequency
, test_type = "wilcox")
pp[[1]] +
theme_classic() +
ylab("Bacteroidetes RA") +
xlab("")
Firmicutes = RAtable %>% filter(Group == "k__Bacteria.p__Firmicutes")
pp = ggplot() +
geom_boxplot(data = Firmicutes, aes(x = Antibiotics, fill = Diet, y = frequency))
boxplot_2_vars_grouped_add_asterix(pp, Firmicutes, "Antibiotics", "frequency", "Diet"
, test = "wilcox", fdr = 1) +
theme_classic() +
ylab("Firmicutes RA") +
xlab("")
Firmicutes_noAB = Firmicutes %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() +
geom_boxplot(data = Firmicutes_noAB, aes(x = Diet, fill = Diet, y = frequency))
pp = add_significant_asterix_to_plot_bothType(pp
, Firmicutes_noAB$Diet
, Firmicutes_noAB$frequency
, test_type = "wilcox")
pp[[1]] +
theme_classic() +
ylab("Firmicutes RA") +
xlab("")
FirVsBac = RAtable %>% filter(Group == "k__Bacteria.p__Firmicutes" |
Group == "k__Bacteria.p__Bacteroidetes") %>%
spread(Group, frequency) %>% mutate(ratio = k__Bacteria.p__Firmicutes / k__Bacteria.p__Bacteroidetes)
pp = ggplot() +
geom_boxplot(data = FirVsBac, aes(x = Antibiotics, fill = Diet, y = ratio))
boxplot_2_vars_grouped_add_asterix(pp, FirVsBac, "Antibiotics", "ratio", "Diet"
, test = "wilcox", fdr = 1) +
theme_classic() +
ylab("Firmicutes/Bacteroidetes RA") +
xlab("")
FirVsBac_noAB = FirVsBac %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() +
geom_boxplot(data = FirVsBac_noAB, aes(x = Diet, fill = Diet, y = ratio))
pp = add_significant_asterix_to_plot_bothType(pp
, FirVsBac_noAB$Diet
, FirVsBac_noAB$ratio
, test_type = "wilcox"
)
pp[[1]] +
theme_classic() +
ylab("Firmicutes/Bacteroidetes RA") +
xlab("")
level6 = read.csv("data/taxa-bar-plots/level-6.csv")
maaslin2_input_noAB = level6 %>%
filter(Antibiotics == "No") %>%
select(index, contains("k__"))
# names(level5)[60:70]
maaslin2_metadata_noAB = level6 %>%
filter(Antibiotics == "No") %>%
select( index
, Diet
# , Weight
)
names(maaslin2_input_noAB)[1] = "#"
names(maaslin2_metadata_noAB)[1] = "#"
write.table( maaslin2_input_noAB
, "data/maaslin2_input_noAB"
, sep = "\t"
, quote = T
, row.names = F)
write.table( maaslin2_metadata_noAB
, "data/maaslin2_metadata_noAB"
, sep = "\t"
, quote = T
, row.names = F)
Maaslin2::Maaslin2("data/maaslin2_input_noAB"
, "data/maaslin2_metadata_noAB"
, "output_maaslin_noAB_weight"
# , random_effects = "Weight"
)maaslin_results = read.table("output_maaslin_noAB_weight/significant_results.tsv"
, header = T)
# View(maaslin_results)
file.remove("data/maaslin2_input_noAB", "data/maaslin2_metadata_noAB")Read biom table and make relative abundances table from it.
source("../scripts/extract.biom.table.R")
library(funrar)
extract.biom.table("/pita/users/rotem/firsttry/res/table.qza")
biom = read.table("/pita/users/rotem/firsttry/res/table.tsv"
, skip = 1, sep = "\t", comment.char = "",
header = T, stringsAsFactors=FALSE)
names(biom)[1] = "SampleID"
SVs = biom$SampleID
biom = data.frame(t(biom))
biom = biom[-1,]
colnames(biom) = SVs
biom = cbind("SampleID" = row.names(biom), biom)
biom[2:length(biom)] = lapply(biom[2:length(biom)]
, function(x) as.numeric(as.character(x)))
RAbiom = biom %>% select(-1) %>%
as.matrix() %>% make_relative() %>%
as.data.frame()
RAbiom = cbind("SampleID" = biom$SampleID, RAbiom)
source("../scripts/extract.biom.table.R")
library(funrar)
extract.biom.table("/pita/users/rotem/firsttry/res/table.qza")error in running commandsh: 1: qiime: not found
sh: 1: biom: not found
error in running commandbiom = read.table("/pita/users/rotem/firsttry/res/table.tsv"
, skip = 1, sep = "\t", comment.char = "",
header = T, stringsAsFactors=FALSE)
names(biom)[1] = "SampleID"
SVs = biom$SampleID
biom = data.frame(t(biom))
biom = biom[-1,]
colnames(biom) = SVs
biom = cbind("SampleID" = row.names(biom), biom)
biom[2:length(biom)] = lapply(biom[2:length(biom)]
, function(x) as.numeric(as.character(x)))
RAbiom = biom %>% select(-1) %>%
as.matrix() %>% make_relative() %>%
as.data.frame()
RAbiom = cbind("SampleID" = biom$SampleID, RAbiom)
write.table( biom_table
, "data/biom_table"
, sep = "\t"
, quote = T
, row.names = F)
write.table( mapping
, "data/mapping"
, sep = "\t"
, quote = T
, row.names = F)
Maaslin2::Maaslin2("data/biom_table"
, "data/mapping"
, "output_maaslin_SV_noAB"
)maaslin_results = read.table("output_maaslin_SV_noAB/all_results.tsv"
, header = T)
# mapping_file = read.table("../firsttry/res/map.csv", header = T)
mapping_file = read.table("/pita/users/rotem/firsttry/res/map.csv"
, header = T)
levels(mapping_file$Type) = c("Ctrl", "Ctrl+AB", "Fructose", "Fructose+AB")
biom_meta = left_join(RAbiom, mapping_file, by = "SampleID")
biom_table =
biom_meta %>%
filter(Antibiotics == "No") %>%
select(SampleID, matches("[ATGC]{10,}"))
mapping = mapping_file %>%
filter(Antibiotics == "No") %>%
select(SampleID, Diet)taxonomy_levels = c("kingdom", "phylum","class","order"
,"family", "genus", "specie")
pattern = "[kpcofgs]__"
extract.qiime("/pita/users/rotem/firsttry/res/taxonomy.qzv", "*tsv")'/pita/users/rotem/firsttry/res/taxonomy' already exists11 files moved. 0 failed.taxonomy = read.csv("/pita/users/rotem/firsttry/res/taxonomy/input.tsv"
, sep = "\t") %>%
separate(Taxon, sep =";", into = taxonomy_levels)Expected 7 pieces. Missing pieces filled with `NA` in 120 rows [1, 6, 11, 29, 50, 53, 61, 79, 81, 93, 96, 98, 99, 108, 119, 121, 126, 136, 138, 142, ...].# taxonomy = read.csv("../firsttry/res/taxonomy/input.tsv"
# , sep = "\t") %>%
# separate(Taxon, sep =";", into = taxonomy_levels)
taxonomy = taxonomy[-1,]
taxonomy = sapply(taxonomy, as.character)
taxonomy[is.na(taxonomy)] = ""
taxonomy = data.frame(taxonomy)
# taxonomy[,taxonomy_levels] =
# as.data.frame(lapply(taxonomy %>% select(taxonomy_levels)
# , str_remove, pattern))
high_maaslin =
maaslin_results %>%
filter(qval < 0.05) %>%
select(feature)
RAbiom_high_maaslin =
biom_meta %>% filter(Antibiotics == "No") %>%
select( high_maaslin %>%
unlist() %>%
as.character(), SampleID)
RAbiom_high_maaslin =
RAbiom_high_maaslin %>%
left_join(mapping_file %>%
select(SampleID, Diet, Antibiotics, Rat_Number)
, by = "SampleID") %>%
filter(Antibiotics == "No")
melted_maaslin =
reshape2::melt( RAbiom_high_maaslin
, id = c("SampleID", "Diet", "Antibiotics", "Rat_Number")
, variable.name = "feature"
, value.name = "RA")
# get taxa names
source("../scripts/get.last.name.R")
names(taxonomy)[1] = "feature"
taxa_names =
data.frame( taxa = unlist(lapply(high_maaslin$feature
, get.last.name, taxonomy))
, feature = high_maaslin$feature)
melted_maaslin = left_join(melted_maaslin, taxa_names, by = "feature")
# Nice samples names
melted_maaslin = melted_maaslin %>%
mutate(code = paste(Diet, paste0("#",Rat_Number)))
# Normalize RA values (just in case)
melted_maaslin =
melted_maaslin %>% group_by(feature) %>% mutate(norm_RA = scale(RA))
melted_maaslin$feature = factor(melted_maaslin$feature)biom_tableAB =
biom_meta %>%
filter(Antibiotics == "Yes") %>%
select(SampleID, matches("[ATGC]{10,}"))
mappingAB = mapping_file %>%
filter(Antibiotics == "Yes") %>%
select(SampleID, Diet)write.table( biom_tableAB
, "data/biom_tableAB"
, sep = "\t"
, quote = T
, row.names = F)
write.table( mappingAB
, "data/mappingAB"
, sep = "\t"
, quote = T
, row.names = F)
Maaslin2::Maaslin2("data/biom_tableAB"
, "data/mappingAB"
, "output_maaslin_SV_AB"
)maaslin_resultsAB = read.table("output_maaslin_SV_AB/all_results.tsv"
, header = T)
names(maaslin_resultsAB)[9]
temp = maaslin_resultsAB[maaslin_resultsAB$qval < .1 & ! is.na(maaslin_resultsAB$qval),]$feature
# tempb = maaslin_resultsAB %>% filter(qval < .05) %>% select(feature) %>% unlist()
high_maaslinAB =
maaslin_resultsAB %>%
filter(qval < 0.05) %>%
select(feature) # %>%
# unlist()
RAbiom_high_maaslinAB =
biom_meta %>% filter(Antibiotics == "Yes") %>%
select( high_maaslinAB %>%
unlist() %>%
as.character(), SampleID)
RAbiom_high_maaslinAB =
RAbiom_high_maaslinAB %>%
# rownames_to_column("SampleID") %>%
left_join(mapping_file %>%
select(SampleID, Diet, Antibiotics, Rat_Number)
, by = "SampleID")
melted_maaslinAB =
reshape2::melt( RAbiom_high_maaslinAB
, id = c("SampleID", "Diet", "Antibiotics", "Rat_Number")
, variable.name = "feature"
, value.name = "RA")
# get taxa names
source("../scripts/get.last.name.R")
names(taxonomy)[1] = "feature"
taxa_namesAB =
data.frame( taxa = unlist(lapply(high_maaslinAB$feature
, get.last.name, taxonomy))
, feature = high_maaslinAB$feature)
melted_maaslinAB = left_join(melted_maaslinAB, taxa_namesAB)Joining, by = "feature"# Nice samples names
melted_maaslinAB = melted_maaslinAB %>%
mutate(code = paste(Diet, paste0("#",Rat_Number)))
# Normalize RA values (just in case)
melted_maaslinAB =
melted_maaslinAB %>% group_by(feature) %>% mutate(norm_RA = scale(RA))
melted_maaslinAB$feature = factor(melted_maaslinAB$feature)
ggplot(melted_maaslinAB, aes( x = code
, y = feature
, fill = log(RA+1e-11)
# , group = taxa
, group = Diet)) +
geom_tile() +
scale_fill_continuous(type = "viridis", name = "log(RA)") +
scale_y_discrete(labels = factor(melted_maaslinAB$taxa)[order(melted_maaslinAB$code)]) +
# scale_x_discrete(labels = factor(melted_maaslin$Diet)[order(melted_maaslin$code)]) +
ggtitle("With Antibiotics") +
theme(
# axis.text.y = element_blank()
axis.text.x = element_blank()
, axis.title.x = element_blank()
)
ggsave(filename = "maaslin_noAB.svg", height = 7, width = 5)
unweighted_path = "/pita/users/rotem/firsttry/res/core-metrics-results/unweighted_unifrac_pcoa_results.qza"
extract.qiime(unweighted_path, "*txt")1 file moved. 0 failed.pcoa = read.table("/pita/users/rotem/firsttry/res/core-metrics-results/unweighted_unifrac_pcoa_results/ordination.txt"
, skip = 9, nrows = length(mapping_file$SampleID), row.names = 1)
var_explained = read.table("/pita/users/rotem/firsttry/res/core-metrics-results/unweighted_unifrac_pcoa_results/ordination.txt", skip = 4, nrows = 1)
pco1 = paste0("PCoA 1 (", round(var_explained[1,1]*100), "%)")
pco2 = paste0("PCoA 2 (", round(var_explained[1,2]*100), "%)")
pco3 = paste0("PCoA 3 (", round(var_explained[1,3]*100), "%)")ggplot(melted_maaslin, aes( x = code
, y = feature
, fill = log(RA+1e-11)
# , group = taxa
, group = Diet)) +
geom_tile() +
scale_fill_continuous(type = "viridis", name = "log(RA)") +
scale_y_discrete(labels = factor(melted_maaslin$taxa)[order(melted_maaslin$code)]) +
# scale_x_discrete(labels = factor(melted_maaslin$Diet)[order(melted_maaslin$code)]) +
ggtitle("No Antibiotics") +
theme(
# axis.text.y = element_blank()
axis.text.x = element_blank()
, axis.title.x = element_blank()
)
# ggsave(filename = "maaslin_noAB.svg", height = 7, width = 6)
pcoa %>%
rownames_to_column("SampleID") %>%
left_join(mapping_file, by = "SampleID") %>%
ggplot(aes(color = Type, x = V2, y = V3)) +
geom_point(size = 4) +
xlab(pco1) +
ylab(pco2) +
theme_classic()
pcoa %>%
rownames_to_column("SampleID") %>%
left_join(mapping_file, by = "SampleID") %>%
ggplot(aes(color = Type, x = V2, y = V4)) +
geom_point(size = 4) +
xlab(pco1) +
ylab(pco3) +
theme_classic()
---
title: "R Notebook"
output: html_notebook
---
untill here all the analysis done on the server according to Tzippy pipeline based on "Moving Pictures" tutorial from [QIIME2 site](https://docs.qiime2.org/2019.4/tutorials/moving-pictures/). The pipeline run with `run-analysys.sh` at `172.35.15.214/pita/users/rotem/firsttry`.
The data copied and unzipped to the `data` directory.
```{bash, include = FALSE}
#../miniconda3/bin/conda init

#../miniconda3/bin/conda activate qiime2-2019.4

```

```{r functions}
#' extract qiime files by a specific file extension
#'
#' @param filepath Path of the qza/v file to be extract
#' @param extension File extension to unzipped from filepath. format \code{'*csv'}.
#' @return Extracted files with \code{extension} in the qiime \code{filepath} file.
#' @examples
#' extract.qiime("path\to\qiime\file\qzv", extension = "*csv")
extract.qiime = function (filepath, extension) {
  library(tools)
  library(filesstrings)
  shortname = file_path_sans_ext(filepath)
  tozip = unzip(filepath, exdir = "data", list = T)[,1]
  unzip(  filepath
        , exdir = "data/"
        , files = tozip[grepl(pattern = extension, tozip)]
        , overwrite = T)
  
  dir.create(shortname)
  unzipped = list.files(  pattern = extension
                        , full.names = T
                        , recursive = T
                        , include.dirs = T)
  
  
  file.move(files = unzipped, destinations = shortname, overwrite = TRUE)
  system("find . -type d -empty -delete")
}

#' Return last taxa names for specific SV
#'
#' @param SV The requested SV.
#' @param taxonomy_table taxonomy table with KPCOFGS order, with confidence level at last column and SV at the first, taxa levels unknown should be empty.
#' @param depth Optional, the depth of the taxonomical level to be returned.
#' @return The last [depth=2] non empty taxonomical values for specific SV expect of specie level.
#' @examples
#' get.last.name(SV = "AAAAAAAAAA", taxonomy_table, depth =2)
get.last.name = function(SV, taxonomy_table, depth = 2) {
  taxonomy_levels = c("kingdom", "phylum","class","order"
                      ,"family", "genus", "specie")
  taxonomy_table = taxonomy_table[,c("feature", taxonomy_levels)]
  specific = taxonomy_table[taxonomy_table[,1] == SV,]
  specific = specific[specific != ""]
  specific = specific[specific != " "]
  specific = specific[!is.na(specific)]
  specific = specific[nchar(specific) > 4]
  rev(specific)[1:depth]
  
  paste(rev(rev(specific)[1:depth]), collapse = " ")
}

```

```{r}
library(tidyverse)
extract.qiime(filepath = "data/taxa-bar-plots.qzv", extension = "*csv")
extract.qiime("data/taxa-bar-plots.qzv", "*tsv")

```

```{r, include = FALSE}
level3 = read.csv("data/taxa-bar-plots/level-3.csv")

datm = level3 %>% 
  select(Type, contains("k__")) %>% 
  gather(Group, "value", -Type) %>% 
  group_by(Type, Group) %>% summarise(median = median(value))

ggplot(datm, aes(x = Type, y = median, fill = Group)) + 
    geom_bar(position = "fill",stat = "identity") +
    scale_y_continuous(labels = scales::percent_format()) +
    theme_classic()
  
```

```{r}
source("../scripts/add_sig_asterix_boxplot_funcs.R")

level2 = read.csv("data/taxa-bar-plots/level-2.csv")

datm = level2 %>% 
  select(index, Type, Diet, Antibiotics, contains("k__"))# %>% 
  #gather(Group, value = number,-index,  -Type, -Diet, -Antibiotics) %>% 
  #group_by(index, Group) %>% summarize(RA = number / sum(number))
  #group_by(Type, Group) %>% summarise(median = median(value)) %>% 
  
  RAtable = datm %>% select(index, Type, Diet, Antibiotics) %>% 
    bind_cols(prop.table(datm %>%
                           select(contains("k__")) %>% 
                           as.matrix(), 1)  %>%
                as.data.frame()) %>% 
    gather(Group, value = frequency, -index,  -Type, -Diet, -Antibiotics)
#RAtable$Type = factor(medianRA$Type, levels =c("Control", "Fructose", "Control_AB",  "Fructose_AB") )
  

    medianRA = RAtable %>% 
             group_by(Type, Group) %>% 
             summarise(median = median(frequency))
  medianRA$Type = factor(medianRA$Type, levels =c("Control", "Fructose", "Control_AB",  "Fructose_AB") )
  ggplot() + 
    geom_bar(data = medianRA
           , aes(y = median, x = Type, fill = Group)
           , stat = "identity", position="fill") + 
  theme_classic()
```
```{r}
levels(RAtable$Antibiotics) = c("No Antibiotics", "Antibiotics")
proteo = RAtable %>% filter(Group ==  "k__Bacteria.p__Proteobacteria")
```

```{r, include = FALSE}
levels(RAtable$Antibiotics) = c("No Antibiotics", "Antibiotics")
proteo = RAtable %>% filter(Group ==  "k__Bacteria.p__Proteobacteria")
p = ggplot() + 
  geom_boxplot(data = proteo, aes(fill = Diet, y = frequency)) + 
  ylab("alpha diversity frequency") +
  facet_wrap( ~ Antibiotics + Diet, strip.position="bottom", nrow = 1) +
  theme_classic() +
  
  theme(axis.text.x = element_blank(), axis.ticks.x = element_line(size = 0))

  add_significant_asterix_to_plot_v2(p, RAtable$Diet, RAtable$frequency)

#   ggplot(datm, aes(x = Type, y = RA, fill = Group)) + 
#     geom_bar(position = "fill",stat = "identity") +
#     scale_y_continuous(labels = scales::percent_format()) + 
#     ggtitle("Taxa L2 by Type Median")+
#     theme_classic()
# 
# 
# 
# ggplot() +
#   geom_boxplot(data = proteo, aes(y = RA, x = Type, fill = Type))
# 
# proteo = datm %>% filter(Group ==  "k__Bacteria.p__Proteobacteria")
# ggplot() +
#   geom_boxplot(data = proteo, aes(y = RA, x = Type, fill = Type))

```

```{r}
source("../scripts/asteriks.R")

pp = ggplot() + 
geom_boxplot(data = proteo, aes(x = Antibiotics, fill = Diet, y = frequency)) 
boxplot_2_vars_grouped_add_asterix(pp, proteo, "Antibiotics", "frequency", "Diet"
                                   , test = "wilcox", fdr = 1) +
  theme_classic() + 
  ylab("Proteobacteria RA") + 
  xlab("")

proteo_noAB = proteo %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() + 
geom_boxplot(data = proteo_noAB, aes(x = Diet, fill = Diet, y = frequency)) 
pp = add_significant_asterix_to_plot_bothType(pp
                                              , proteo_noAB$Diet
                                              , proteo_noAB$frequency
                                              , test_type = "wilcox")
pp[[1]] +   
  theme_classic() + 
  ylab("Proteobacteria RA") + 
  xlab("")

```

```{r}
Bacteroidetes = RAtable %>% filter(Group ==  "k__Bacteria.p__Bacteroidetes")
pp = ggplot() + 
geom_boxplot(data = Bacteroidetes, aes(  x = Antibiotics 
                                       , fill = Diet
                                       , y = frequency)) 
boxplot_2_vars_grouped_add_asterix(pp, Bacteroidetes, "Antibiotics"
                                   , "frequency", "Diet" , test = "wilcox", fdr = 1) +
  theme_classic() + 
  ylab("Bacteroidetes RA") + 
  xlab("")

Bacteroidetes_noAB = Bacteroidetes %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() + 
geom_boxplot(data = Bacteroidetes_noAB, aes(x = Diet, fill = Diet, y = frequency)) 
pp = add_significant_asterix_to_plot_bothType(pp
                                              , Bacteroidetes_noAB$Diet
                                              , Bacteroidetes_noAB$frequency
                                              , test_type = "wilcox")
pp[[1]] +   
  theme_classic() + 
  ylab("Bacteroidetes RA") + 
  xlab("")

```

```{r}
Firmicutes = RAtable %>% filter(Group ==  "k__Bacteria.p__Firmicutes")
pp = ggplot() + 
geom_boxplot(data = Firmicutes, aes(x = Antibiotics, fill = Diet, y = frequency)) 
boxplot_2_vars_grouped_add_asterix(pp, Firmicutes, "Antibiotics", "frequency", "Diet"
                                   , test = "wilcox", fdr = 1) +
  theme_classic() + 
  ylab("Firmicutes RA") + 
  xlab("")

Firmicutes_noAB = Firmicutes %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() + 
geom_boxplot(data = Firmicutes_noAB, aes(x = Diet, fill = Diet, y = frequency)) 
pp = add_significant_asterix_to_plot_bothType(pp
                                              , Firmicutes_noAB$Diet
                                              , Firmicutes_noAB$frequency
                                              , test_type = "wilcox")
pp[[1]] +   
  theme_classic() + 
  ylab("Firmicutes RA") + 
  xlab("")

```
```{r}
FirVsBac =  RAtable %>% filter(Group ==  "k__Bacteria.p__Firmicutes" |
                                 Group ==  "k__Bacteria.p__Bacteroidetes") %>% 
  spread(Group, frequency) %>% mutate(ratio = k__Bacteria.p__Firmicutes / k__Bacteria.p__Bacteroidetes)
pp = ggplot() + 
geom_boxplot(data = FirVsBac, aes(x = Antibiotics, fill = Diet, y = ratio)) 
boxplot_2_vars_grouped_add_asterix(pp, FirVsBac, "Antibiotics", "ratio", "Diet"
                                   , test = "wilcox", fdr = 1) +
  theme_classic() + 
  ylab("Firmicutes/Bacteroidetes RA") + 
  xlab("")

FirVsBac_noAB = FirVsBac %>% filter(Antibiotics == "No Antibiotics")
pp = ggplot() + 
geom_boxplot(data = FirVsBac_noAB, aes(x = Diet, fill = Diet, y = ratio)) 
pp = add_significant_asterix_to_plot_bothType(pp
                                              , FirVsBac_noAB$Diet
                                              , FirVsBac_noAB$ratio
                                              , test_type = "wilcox"
                                              )
pp[[1]] +   
  theme_classic() + 
  ylab("Firmicutes/Bacteroidetes RA") + 
  xlab("")

```

```{r, include = FALSE}
level7 = read.csv("data/taxa-bar-plots/level-7.csv")
datm = level7 %>% 
  select(Type, contains("k__")) %>% 
  gather(Group, "value", -Type) %>% 
  group_by(Type, Group) %>% summarise(median = median(value))

datm %>% filter(Group == "k__Bacteria.p__Proteobacteria")

datm$Group = levels(factor(datm$Group))
ggplot() + geom_boxplot(data = datm, aes(x = Type, y = median, fill = Type)) + 
  xlab("")
 
```

```{r, results = "hide"}
level6 = read.csv("data/taxa-bar-plots/level-6.csv")
maaslin2_input_noAB = level6 %>% 
    filter(Antibiotics == "No") %>%
    select(index, contains("k__"))
# names(level5)[60:70]
maaslin2_metadata_noAB = level6 %>%
    filter(Antibiotics == "No") %>%
    select(  index
           , Diet
           # , Weight
           )

names(maaslin2_input_noAB)[1] = "#"
names(maaslin2_metadata_noAB)[1] = "#"

write.table(  maaslin2_input_noAB
            , "data/maaslin2_input_noAB"
            , sep = "\t"
            , quote = T
            , row.names = F)
write.table(  maaslin2_metadata_noAB
            , "data/maaslin2_metadata_noAB"
            , sep = "\t"
            , quote = T
            , row.names = F)

Maaslin2::Maaslin2("data/maaslin2_input_noAB"
    , "data/maaslin2_metadata_noAB"
    , "output_maaslin_noAB_weight"
    # , random_effects = "Weight"
)

maaslin_results = read.table("output_maaslin_noAB_weight/significant_results.tsv"
                             , header = T)
# View(maaslin_results)
file.remove("data/maaslin2_input_noAB", "data/maaslin2_metadata_noAB")
```
Read biom table and make relative abundances table from it.
```{r, biom-table}
source("../scripts/extract.biom.table.R")
library(funrar)

extract.biom.table("/pita/users/rotem/firsttry/res/table.qza")

biom = read.table("/pita/users/rotem/firsttry/res/table.tsv"
                  , skip = 1, sep = "\t", comment.char = "",
                  header = T, stringsAsFactors=FALSE)

names(biom)[1] = "SampleID"
SVs = biom$SampleID

biom = data.frame(t(biom))
biom = biom[-1,]
colnames(biom) = SVs
biom = cbind("SampleID" = row.names(biom), biom)
biom[2:length(biom)] = lapply(biom[2:length(biom)]
                              , function(x) as.numeric(as.character(x)))

RAbiom = biom %>% select(-1) %>% 
  as.matrix() %>% make_relative() %>% 
  as.data.frame()

RAbiom = cbind("SampleID" = biom$SampleID, RAbiom)


```

```{r}
# mapping_file = read.table("../firsttry/res/map.csv", header = T)
mapping_file = read.table("/pita/users/rotem/firsttry/res/map.csv"
                          , header = T)
levels(mapping_file$Type) = c("Ctrl", "Ctrl+AB", "Fructose", "Fructose+AB")


biom_meta = left_join(RAbiom, mapping_file, by = "SampleID")

biom_table = 
  biom_meta %>% 
    filter(Antibiotics == "No") %>% 
    select(SampleID, matches("[ATGC]{10,}"))

mapping = mapping_file %>% 
  filter(Antibiotics == "No") %>% 
  select(SampleID, Diet)
```

```{r run-maaslin, results = "hide", message = F}
write.table(  biom_table
            , "data/biom_table"
            , sep = "\t"
            , quote = T
            , row.names = F)


write.table(  mapping
            , "data/mapping"
            , sep = "\t"
            , quote = T
            , row.names = F)

Maaslin2::Maaslin2("data/biom_table"
    , "data/mapping"
    , "output_maaslin_SV_noAB"
    )

maaslin_results = read.table("output_maaslin_SV_noAB/all_results.tsv"
                            , header = T)

```

```{r}
taxonomy_levels = c("kingdom", "phylum","class","order"
                    ,"family", "genus", "specie")
pattern = "[kpcofgs]__"
extract.qiime("/pita/users/rotem/firsttry/res/taxonomy.qzv", "*tsv")
taxonomy = read.csv("/pita/users/rotem/firsttry/res/taxonomy/input.tsv"
                    , sep = "\t") %>% 
              separate(Taxon, sep =";", into = taxonomy_levels)

# taxonomy = read.csv("../firsttry/res/taxonomy/input.tsv"
#                     , sep = "\t") %>% 
#               separate(Taxon, sep =";", into = taxonomy_levels)

taxonomy = taxonomy[-1,]


taxonomy = sapply(taxonomy, as.character)
taxonomy[is.na(taxonomy)] = ""
taxonomy = data.frame(taxonomy)

# taxonomy[,taxonomy_levels] = 
# as.data.frame(lapply(taxonomy %>% select(taxonomy_levels)
#                      , str_remove, pattern))

```

```{r, warning = F}
high_maaslin = 
  maaslin_results %>% 
    filter(qval < 0.05) %>%
    select(feature)

RAbiom_high_maaslin = 
  biom_meta %>% filter(Antibiotics == "No") %>% 
                select( high_maaslin %>%
                        unlist() %>%
                        as.character(), SampleID)

RAbiom_high_maaslin = 
  RAbiom_high_maaslin %>% 
  left_join(mapping_file %>% 
              select(SampleID, Diet, Antibiotics, Rat_Number)
                , by = "SampleID") %>% 
  filter(Antibiotics == "No")
  
melted_maaslin = 
  reshape2::melt(  RAbiom_high_maaslin
                 , id = c("SampleID", "Diet", "Antibiotics", "Rat_Number")
                 , variable.name = "feature"
                 , value.name = "RA")
# get taxa names
source("../scripts/get.last.name.R")
names(taxonomy)[1] = "feature"
taxa_names = 
  data.frame(  taxa = unlist(lapply(high_maaslin$feature
                                  , get.last.name, taxonomy))
             , feature = high_maaslin$feature)

melted_maaslin = left_join(melted_maaslin, taxa_names, by = "feature")
# Nice samples names
melted_maaslin = melted_maaslin %>% 
      mutate(code = paste(Diet, paste0("#",Rat_Number)))
# Normalize RA values (just in case)
melted_maaslin = 
  melted_maaslin %>% group_by(feature) %>% mutate(norm_RA = scale(RA))

melted_maaslin$feature = factor(melted_maaslin$feature)
```
```{r, warning = F}
ggplot(melted_maaslin, aes(  x = code
                           , y = feature
                           , fill = log(RA+1e-11)
                           # , group = taxa
                           , group = Diet)) +
  geom_tile() +
  scale_fill_continuous(type = "viridis", name = "log(RA)") + 
  scale_y_discrete(labels = factor(melted_maaslin$taxa)[order(melted_maaslin$code)]) + 
  # scale_x_discrete(labels = factor(melted_maaslin$Diet)[order(melted_maaslin$code)]) +
  ggtitle("No Antibiotics") + 
  theme(  
    # axis.text.y = element_blank()
         axis.text.x = element_blank()
        , axis.title.x = element_blank()
       )

# ggsave(filename = "maaslin_noAB.svg", height = 7, width = 6)  

```
```{r make-biom-table-for-AB}
biom_tableAB = 
  biom_meta %>% 
    filter(Antibiotics == "Yes") %>% 
    select(SampleID, matches("[ATGC]{10,}"))

mappingAB = mapping_file %>% 
  filter(Antibiotics == "Yes") %>% 
  select(SampleID, Diet)
```

```{r run-maaslin-AB, results = "hide", message = F}
write.table(  biom_tableAB
            , "data/biom_tableAB"
            , sep = "\t"
            , quote = T
            , row.names = F)

write.table(  mappingAB
            , "data/mappingAB"
            , sep = "\t"
            , quote = T
            , row.names = F)

Maaslin2::Maaslin2("data/biom_tableAB"
    , "data/mappingAB"
    , "output_maaslin_SV_AB"
    )

maaslin_resultsAB = read.table("output_maaslin_SV_AB/all_results.tsv"
                               , header = T)

names(maaslin_resultsAB)[9]
temp = maaslin_resultsAB[maaslin_resultsAB$qval < .1 & ! is.na(maaslin_resultsAB$qval),]$feature

# tempb = maaslin_resultsAB %>% filter(qval < .05) %>% select(feature) %>% unlist()


```

```{r mmaslin-heatmap-AB, warning = F}
high_maaslinAB = 
maaslin_resultsAB %>% 
  filter(qval < 0.05) %>%
  select(feature) # %>%
  # unlist()

RAbiom_high_maaslinAB = 
  biom_meta %>% filter(Antibiotics == "Yes") %>% 
                select( high_maaslinAB %>%
                        unlist() %>%
                        as.character(), SampleID)

RAbiom_high_maaslinAB = 
  RAbiom_high_maaslinAB %>% 
  # rownames_to_column("SampleID") %>% 
  left_join(mapping_file %>% 
              select(SampleID, Diet, Antibiotics, Rat_Number)
                , by = "SampleID") 
  
melted_maaslinAB = 
  reshape2::melt(  RAbiom_high_maaslinAB
                 , id = c("SampleID", "Diet", "Antibiotics", "Rat_Number")
                 , variable.name = "feature"
                 , value.name = "RA")
# get taxa names
source("../scripts/get.last.name.R")
names(taxonomy)[1] = "feature"
taxa_namesAB = 
  data.frame(  taxa = unlist(lapply(high_maaslinAB$feature
                                  , get.last.name, taxonomy))
             , feature = high_maaslinAB$feature)
melted_maaslinAB = left_join(melted_maaslinAB, taxa_namesAB)
# Nice samples names
melted_maaslinAB = melted_maaslinAB %>% 
      mutate(code = paste(Diet, paste0("#",Rat_Number)))
# Normalize RA values (just in case)
melted_maaslinAB = 
  melted_maaslinAB %>% group_by(feature) %>% mutate(norm_RA = scale(RA))

melted_maaslinAB$feature = factor(melted_maaslinAB$feature)

ggplot(melted_maaslinAB, aes(  x = code
                           , y = feature
                           , fill = log(RA+1e-11)
                           # , group = taxa
                           , group = Diet)) +
  geom_tile() +
  scale_fill_continuous(type = "viridis", name = "log(RA)") + 
  scale_y_discrete(labels = factor(melted_maaslinAB$taxa)[order(melted_maaslinAB$code)]) + 
  # scale_x_discrete(labels = factor(melted_maaslin$Diet)[order(melted_maaslin$code)]) +
  ggtitle("With Antibiotics") + 
  theme(  
    # axis.text.y = element_blank()
         axis.text.x = element_blank()
        , axis.title.x = element_blank()
       )

ggsave(filename = "maaslin_noAB.svg", height = 7, width = 5)  

```


```{r unweighted-unifrac, warning = F}
unweighted_path = "/pita/users/rotem/firsttry/res/core-metrics-results/unweighted_unifrac_pcoa_results.qza"
extract.qiime(unweighted_path, "*txt")

pcoa = read.table("/pita/users/rotem/firsttry/res/core-metrics-results/unweighted_unifrac_pcoa_results/ordination.txt"
                  , skip = 9, nrows = length(mapping_file$SampleID), row.names = 1)


var_explained = read.table("/pita/users/rotem/firsttry/res/core-metrics-results/unweighted_unifrac_pcoa_results/ordination.txt", skip = 4, nrows = 1)

pco1 = paste0("PCoA 1 (", round(var_explained[1,1]*100), "%)")
pco2 = paste0("PCoA 2 (", round(var_explained[1,2]*100), "%)")
pco3 = paste0("PCoA 3 (", round(var_explained[1,3]*100), "%)")
```

```{r, warning = F}
pcoa %>% 
  rownames_to_column("SampleID") %>%
  left_join(mapping_file, by = "SampleID") %>% 
  ggplot(aes(color = Type, x = V2, y = V3)) + 
  geom_point(size = 4) + 
  xlab(pco1) + 
  ylab(pco2) + 
  theme_classic()
```

```{r, warning = F}
pcoa %>% 
  rownames_to_column("SampleID") %>%
  left_join(mapping_file, by = "SampleID") %>% 
  ggplot(aes(color = Type, x = V2, y = V4)) + 
  geom_point(size = 4) + 
  xlab(pco1) + 
  ylab(pco3) + 
  theme_classic()
```

```{r, warning = F}
pcoa %>% 
  rownames_to_column("SampleID") %>%
  left_join(mapping_file, by = "SampleID") %>% 
  ggplot(aes(color = Type, x = V3, y = V4)) + 
  geom_point(size = 4) + 
  xlab(pco2) + 
  ylab(pco3) + 
  theme_classic()
```

```{r, include = FALSE}
RAbiom_high_maaslin_t = RAbiom_high_maaslin %>% t() %>% data.frame()
names(RAbiom_high_maaslin_t) = factor(unlist(RAbiom_high_maaslin_t[1,]))

RAbiom_high_maaslin_t = RAbiom_high_maaslin_t[-1,]

hm = 
RAbiom_high_maaslin_t %>% 
  rownames_to_column("SV") %>%
  gather(sample = -SV, value = "RA")

ggplot(hm, aes(fill = RA, y = SV, x = key)) + 
  geom_tile() + 
  scale_fill_gradient() + 
  theme(  legend.position = "none"
        , axis.text.y = element_blank())
```


```{r level-5-AB, include = FALSE}
level5 = read.csv("data/taxa-bar-plots/level-5.csv")
maaslin2_input_AB = level5 %>% 
    filter(Antibiotics == "Yes") %>%
    select(index, contains("k__"))
# names(level5)
maaslin2_metadata_AB = level5 %>%
    filter(Antibiotics == "Yes") %>%
    select(index, Diet, Weight)

names(maaslin2_input_AB)[1] = "#"
names(maaslin2_metadata_AB)[1] = "#"

write.table(  maaslin2_input_AB
            , "data/maaslin2_input_AB"
            , sep = "\t"
            , quote = T
            , row.names = F)
write.table(  maaslin2_metadata_AB
            , "data/maaslin2_metadata_AB"
            , sep = "\t"
            , quote = T
            , row.names = F)

Maaslin2::Maaslin2("data/maaslin2_input_AB"
    , "data/maaslin2_metadata_AB"
    , "output_maaslin_AB"
    )

# file.remove("data/maaslin2_input_AB", "data/maaslin2_metadata_AB")

```

```{bash}
``` 

