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app.R
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#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
#detach("package:here", unload=TRUE)
library(shiny)
library(here)
library(ggplot2)
library(tidyverse)
library(vegan)
library(cowplot)
library(ggpubr)
library(mvabund)
library(RColorBrewer)
library(reshape2)
library(purrr)
library(dplyr)
library(plotly)
library(indicspecies)
library(adespatial)
library(fossil)
library(ggdendro)
library(dendextend)
bpps <- 0.6
#dim(otutab_sfsep0)
#otutab_sfsep <- otutab_sfsep0[,c(5:8,1:4,9:122,127:130,123:126,131:151)]
otutab_sfsep <- read.table(here("data", "otutab_3.200all_mtax.txt"), header = T, sep = "\t")
taxlevels <- c("Kingdom", "Supergroup", "Division", "Class", "Order", "Family", "Genus",
"Species", "Divisionlong", "newOTUid", "newOTUid_wgen")
#names(otutab_sfsep)
rm(otutab_sfsep0)
sample_ordersfsep <- tibble(sample = names(otutab_sfsep)[c(1:140)])
####Read in samples names per size fraction####
pico <- readLines(here("data","pico_descnames.txt"))
three10 <- readLines(here("data","three10_descnames.txt"))
three180 <- readLines(here("data","three180_descnames.txt"))
ten50 <- readLines(here("data","ten50_descnames.txt"))
fifty200 <- readLines(here("data","fifty200_descnames.txt"))
three200 <- readLines(here("data", "three200_descnames.txt"))
three200.2 <- three200[!three200 %in% three180]
net_all <- readLines(here("data","net_all_descnames.txt"))
sfnames <- list("sf0.4.3" = pico, "sf3.10" = three10, "sf10.50" = ten50, "sf50.200" = fifty200, "sf3.180" = three180, "sf3.200" = three200)
sfs <- names(sfnames)
#### Length of each size-fractionated data set ####
npico <- length(pico)
nthree10 <- length(three10)
nten50 <- length(ten50)
nfifty200 <- length(fifty200)
nthree180 <- length(three180)
nthree200 <- length(three200)
#### Number of samples in each cruise ####
nmp1 <- 8
nmp2 <- 10
nmp3 <- 10
nmp4 <- 11
nmp5 <- 5
#### names of all sample_sf combinations in each cruise ####
mp1names <- c(pico[c(1:nmp1)], three180[c(1:nmp1)])
mp2names <- c(pico[c((nmp1+1):(nmp1+nmp2))], three180[c((nmp1+1):(nmp1+nmp2))])
mp3names <- c(pico[c((nmp1+nmp2+1):(nmp1+nmp2+nmp3))], three10[c(1:nmp3)], ten50[c(1:nmp3)], fifty200[c(1:nmp3)])
mp4names <- c(pico[c((nmp1+nmp2+nmp3+1):(nmp1+nmp2+nmp3+nmp4))], three10[c((nmp3+1):(nmp3+nmp4))], ten50[c((nmp3+1):(nmp3+nmp4))], fifty200[c((nmp3+1):(nmp3+nmp4))])
mp5names <- c(pico[c((nmp1+nmp2+nmp3+nmp4+1):(nmp1+nmp2+nmp3+nmp4+nmp5))], three10[c((nmp3+nmp4+1):(nmp3+nmp4+nmp5))], ten50[c((nmp3+nmp4+1):(nmp3+nmp4+nmp5))], fifty200[c((nmp3+nmp4+1):(nmp3+nmp4+nmp5))])
#### Read meta table ####
meta_table <- read.table(here("data", "meta_cleanMP.txt"), header = T, sep = "\t", fill = NA)
depthbin0.4.3 <- meta_table %>% filter(sf0.4.3 %in% sfnames$sf0.4.3) %>% pull(depthbin) %>% as.character()
depthbin3.10 <- meta_table %>% filter(sf3.10 %in% sfnames$sf3.10) %>% pull(depthbin) %>% as.character()
depthbin10.50 <- meta_table %>% filter(sf10.50 %in% sfnames$sf10.50) %>% pull(depthbin) %>% as.character()
depthbin50.200 <- meta_table %>% filter(sf50.200 %in% sfnames$sf50.200) %>% pull(depthbin) %>% as.character()
depthbin3.180 <- meta_table %>% filter(sf3.180 %in% sfnames$sf3.180) %>% pull(depthbin) %>% as.character()
depthbinsfsep <- c(depthbin0.4.3, depthbin3.10, depthbin10.50, depthbin50.200, depthbin3.180)
samples0.4.3 <- meta_table %>% filter(sf0.4.3 %in% sfnames$sf0.4.3) %>% pull(sf0.4.3) %>% as.character()
samples3.10 <- meta_table %>% filter(sf3.10 %in% sfnames$sf3.10) %>% pull(sf3.10) %>% as.character()
samples10.50 <- meta_table %>% filter(sf10.50 %in% sfnames$sf10.50) %>% pull(sf10.50) %>% as.character()
samples50.200 <- meta_table %>% filter(sf50.200 %in% sfnames$sf50.200) %>% pull(sf50.200) %>% as.character()
samples3.180 <- meta_table %>% filter(sf3.180 %in% sfnames$sf3.180) %>% pull(sf3.180) %>% as.character()
samplessfsep <- c(samples0.4.3, samples3.10, samples10.50, samples50.200, samples3.180)
depthbinsfsep_tb0 <- tibble(depthbin = depthbinsfsep, sample = samplessfsep)
depthbinsfsep_tb <- left_join(sample_ordersfsep, depthbinsfsep_tb0, by = "sample")
depthlab0.4.3 <- meta_table %>% filter(sf0.4.3 %in% sfnames$sf0.4.3) %>% pull(DEPTH_M) %>% as.character()
depthlab3.10 <- meta_table %>% filter(sf3.10 %in% sfnames$sf3.10) %>% pull(DEPTH_M) %>% as.character()
depthlab10.50 <- meta_table %>% filter(sf10.50 %in% sfnames$sf10.50) %>% pull(DEPTH_M) %>% as.character()
depthlab50.200 <- meta_table %>% filter(sf50.200 %in% sfnames$sf50.200) %>% pull(DEPTH_M) %>% as.character()
depthlab3.180 <- meta_table %>% filter(sf3.180 %in% sfnames$sf3.180) %>% pull(DEPTH_M) %>% as.character()
depthlabsfsep <- c(depthlab0.4.3, depthlab3.10, depthlab10.50, depthlab50.200, depthlab3.180)
depthlabsfsep_tb0 <- tibble(depthlab = depthlabsfsep, sample = samplessfsep)
depthlabsfsep_tb <- left_join(sample_ordersfsep, depthlabsfsep_tb0, by = "sample")
cruise0.4.3 <- meta_table %>% filter(sf0.4.3 %in% sfnames$sf0.4.3) %>% pull(Cruise) %>% as.character()
cruise3.10 <- meta_table %>% filter(sf3.10 %in% sfnames$sf3.10) %>% pull(Cruise) %>% as.character()
cruise10.50 <- meta_table %>% filter(sf10.50 %in% sfnames$sf10.50) %>% pull(Cruise) %>% as.character()
cruise50.200 <- meta_table %>% filter(sf50.200 %in% sfnames$sf50.200) %>% pull(Cruise) %>% as.character()
cruise3.180 <- meta_table %>% filter(sf3.180 %in% sfnames$sf3.180) %>% pull(Cruise) %>% as.character()
cruisesfsep <- c(cruise0.4.3, cruise3.10, cruise10.50, cruise50.200, cruise3.180)
cruisesfsep_tb0 <- tibble(cruise = cruisesfsep, sample = samplessfsep)
cruisesfsep_tb <- left_join(sample_ordersfsep, cruisesfsep_tb0, by = "sample")
Station0.4.3 <- meta_table %>% filter(sf0.4.3 %in% sfnames$sf0.4.3) %>% pull(Station) %>% as.character()
Station3.10 <- meta_table %>% filter(sf3.10 %in% sfnames$sf3.10) %>% pull(Station) %>% as.character()
Station10.50 <- meta_table %>% filter(sf10.50 %in% sfnames$sf10.50) %>% pull(Station) %>% as.character()
Station50.200 <- meta_table %>% filter(sf50.200 %in% sfnames$sf50.200) %>% pull(Station) %>% as.character()
Station3.180 <- meta_table %>% filter(sf3.180 %in% sfnames$sf3.180) %>% pull(Station) %>% as.character()
Stationsfsep <- c(Station0.4.3, Station3.10, Station10.50, Station50.200, Station3.180)
Stationsfsep_tb0 <- tibble(Station = Stationsfsep, sample = samplessfsep)
Stationsfsep_tb <- left_join(sample_ordersfsep, Stationsfsep_tb0, by = "sample")
StationDepthsfsep_tb <- paste(Stationsfsep_tb$Station, depthlabsfsep_tb$depthlab, sep = "_")
StationDepthsfsep_tb <- factor(StationDepthsfsep_tb, ordered = T, levels = unique(StationDepthsfsep_tb))
####
#Chloroplast
chltab <- read.table(here("data", "chltab_prop_mtax.txt"), header = T, sep = "\t", row.names = 1)
chlmeta <- read.table(here("data", "chl_samplesmeta_dnavsrna.txt"), header = T, sep = "\t", row.names = 1)
chlmeta$sample <- rownames(chlmeta)
#Chl meta tables
StationDepths_tb_chl <- paste(chlmeta$station, chlmeta$depth, sep = "_")
StationDepths_tb_chl <- factor(StationDepths_tb_chl, ordered = T, levels = unique(StationDepths_tb_chl))
source(here("src", "nmdsplot.R"))
source(here("src", "mp_nmds.R"))
source(here("src", "taxa_plot.R"))
# Define UI for application that analyses taxonomic composition
#options(error= recover)
#### User interface ####
ui <- navbarPage(
# Application title
"Taxonomic composition of MicroPolar protist samples",
#Tab showing barplot of proportional read abundance and proportional OTU richness
tabPanel("Proportional abundance",
fluidPage(sidebarLayout(
sidebarPanel(
checkboxGroupInput("taxo_group2", label = "Division",
choices = c("Ochrophyta_Bacillariophyta", "Dinoflagellata_Syndiniales",
"Dinoflagellata_Dinophyceae", "Haptophyta", "Ciliophora", "Chlorophyta", "Picozoa", "Stramenopiles_X", "Pseudofungi", "Radiolaria", "All"),
selected = "All"),
radioButtons("taxlevel", label = "Taxonomic level",
choices = c("Division" = "Divisionlong", "Class"="Class", "Order"="Order", "Family"="Family", "Genus"="Genus", "Species"="Species")),
radioButtons("propchoice", label = "Proportion of group or all", choices = c("Selected group" = "selectgroup","All" = "All"))
),
mainPanel(plotlyOutput(outputId = "propPlot1", height = 400, width = 850),
plotlyOutput(outputId = "propPlot2", height = 600, width = 1500),
plotlyOutput(outputId = "propPlotTot",height = 200, width = 1500),
plotlyOutput(outputId = "propPlot_pa1", height = 400, width = 850),
plotlyOutput(outputId = "propPlot_pa2", height = 600, width = 1500),
plotlyOutput(outputId = "propPlotTot_pa",height = 200, width = 1500),
textOutput(outputId = "sjekk_prop")
)))),
#### NMDS tab ####
# Tab with NMDS plot, hclust dendrogram and table with p-values for test of differential OTU composition between depths
tabPanel("Beta diversity - NMDS",
# Sidebar with radio buttons to select size fraction, checkbox to select one or more taxonomic groups
fluidPage(sidebarLayout(
sidebarPanel(
radioButtons("sizefract1", label = "Size fraction",
choices = c("0.4-3" = "sf0.4.3","3-180" = "sf3.180","3-10" = "sf3.10","10-50" = "sf10.50","50-200" = "sf50.200","3-200" = "sf3.200"), selected = NULL),
checkboxGroupInput("taxo_group_clust", label = "Division",
choices = c("Ochrophyta_Bacillariophyta", "Dinoflagellata_Syndiniales",
"Dinoflagellata_Dinophyceae", "Haptophyta", "Ciliophora", "Chlorophyta", "Picozoa",
"Stramenopiles_X", "Radiolaria", "All"),
selected = "All"),
radioButtons("taxlevel_clustplot", label = "Taxonomic level",
choices = c("Class", "Order", "Family", "Genus", "Species")),
numericInput("clusters", "# clusters", 2, min = 2, max = 20, step = NA, width = NULL)
),
mainPanel(
fluidRow(
column(9, plotlyOutput(outputId = "distPlot")),
column(1, textOutput(outputId = "sjekk"))
),
fluidRow(
column(3, textOutput(outputId = "vp.month.depth"))
),
fluidRow(
column(3, textOutput(outputId = "stress"))
),
fluidRow(
column(12, plotOutput(outputId = "dendroplot"))
),
fluidRow(
column(12, plotlyOutput(outputId = "plotclust"))
),
fluidRow(
column(12, plotlyOutput(outputId = "barplot_clust_ra_ly"))
),
fluidRow(
column(12, plotlyOutput(outputId = "richplot_clust_ra_ly"))
),
fluidRow(
column(12, plotOutput(outputId = "slopeplot_clust_ra"))
)
)
))),
#### Multivariate test of different OTU communities between cruises ####
tabPanel("manyglm test - season",
# Sidebar with radio buttons to select size fraction, checkbox to select one or more taxonomic groups
sidebarLayout(
sidebarPanel(
radioButtons("sizefract3", label = "Size fraction",
choices = c("sf0.4.3", "sf3.180", "sf3.10", "sf10.50", "sf50.200"), selected = NULL),
checkboxGroupInput("taxo_group4", label = "Division",
choices = c("Ochrophyta_Bacillariophyta", "Dinoflagellata_Syndiniales",
"Dinoflagellata_Dinophyceae", "Haptophyta", "Ciliophora", "Chlorophyta", "Picozoa", "Stramenopiles_X", "All"),
selected = "All"),
radioButtons("zone", label = "Depth zone", choices = c("epi", "meso"), selected = NULL),
radioButtons("manyglm2", label = "manyglm test of different OTU composition by cruise (may take some time to compute)", choices = c("yes", "no"), selected = "no")),
mainPanel(
tableOutput(outputId = "tax_month_sf_sign_1_zone")
))),
#### Rank-abundance of the OTUs within the selected Division(s). Color code shows the selected taxonomic level. ####
tabPanel("Rank-abundance",
fluidPage(sidebarLayout(
sidebarPanel(
checkboxGroupInput("taxo_group_ra", label = "Division",
choices = c("Ochrophyta_Bacillariophyta", "Dinoflagellata_Syndiniales",
"Dinoflagellata_Dinophyceae", "Haptophyta", "Ciliophora", "Chlorophyta", "Picozoa", "Stramenopiles_X", "Radiolaria", "All"),
selected = "All"),
radioButtons("taxlevel_ra", label = "Taxonomic level",
choices = c("Class"="Class", "Order"="Order", "Family"="Family", "Genus"="Genus", "OTU"="newOTUid_wgen")),
radioButtons("propchoice_ra", label = "Prop. of selected group\n or all",
choices = c("Selectgroup" = "selectgroup", "All" = "All"))
),
mainPanel(
plotlyOutput(outputId = "raPlot1", height = 400, width = 1500),
plotlyOutput(outputId = "raPlot2", height = 600, width = 1500)
)))),
#### alpha diversity (OTU richness) by cruise and size fraction for the selected division(s) ####
tabPanel("Alpha diversity - richness & evenness",
fluidPage(sidebarLayout(
sidebarPanel(
checkboxGroupInput("taxo_group_adiv", label = "Division",
choices = c("Ochrophyta_Bacillariophyta", "Dinoflagellata_Syndiniales",
"Dinoflagellata_Dinophyceae", "Haptophyta", "Ciliophora", "Chlorophyta", "Picozoa", "Stramenopiles_X", "Radiolaria", "All"),
selected = "All")),
mainPanel(
fluidRow(column(5, plotlyOutput(outputId = "richPlot1", height = 350)),
column(7, plotlyOutput(outputId = "richPlot2", height = 600))),
fluidRow(column(5, plotlyOutput(outputId = "evenPlot1", height = 350)),
column(7, plotlyOutput(outputId = "evenPlot2", height = 600)))
)))),
#### "Local contribution to beta diversity" (LCBD) ####
tabPanel("Beta diversity",
fluidPage(sidebarLayout(
sidebarPanel(
radioButtons("sizefract2", label = "Size fraction",
choices = c("sf0.4.3", "sf3.180", "sf3.10", "sf10.50", "sf50.200"), selected = NULL),
checkboxGroupInput("taxo_group_bdiv", label = "Division",
choices = c("Ochrophyta_Bacillariophyta", "Dinoflagellata_Syndiniales",
"Dinoflagellata_Dinophyceae", "Haptophyta", "Ciliophora", "Chlorophyta", "Picozoa", "Stramenopiles_X", "Radiolaria", "All"),
selected = "All")
),
mainPanel("Interpretation of Local contribution to beta diversity (lcbd): see Legendre & De Cáceres (2013)",
fluidRow(column(12, plotlyOutput(outputId = "lcbdPlott", height = 350))),
textOutput(outputId = "BDtotal" ))
))),
#### Proportional read abundance of Chloroplast 16S data (DNA and RNA) ####
tabPanel("Chloroplast 16S data",
sidebarLayout(
sidebarPanel(
checkboxGroupInput("taxo_group3", label = "Division",
choices = c("Chlorophyta",
"Cryptophyta", "Dinophyta", "Discoba",
"Haptophyta", "Ochrophyta", "Rappemonad", "All"),
selected = "All"),
radioButtons("taxlevel_chl", label = "Taxonomic level",
choices = c("Class", "SubClass", "Order", "SubOrder", "Family", "Genus", "Species"))
),
mainPanel(
plotlyOutput(outputId = "propPlotchl", height = 1000, width = 1000)
)
)
),
#### Indicator Value Analysis Multipatt ####
tabPanel("Characteristic OTUs",
sidebarLayout(
sidebarPanel(
# checkboxGroupInput("month", label = "Month",
# choices = c("January", "March", "May", "August", "November", "All"),
# selected = "All"),
radioButtons("sizefract_iva", label = "Size fraction",
choices = c("0.4-3" = "sf0.4.3", "3-180" = "sf3.180", "3-10" = "sf3.10",
"10-50" = "sf10.50", "50-200" = "sf50.200", "3-200" = "sf3.200"),
selected = NULL),
radioButtons("taxo_group_iva", label = "Division",
choices = c("Ochrophyta_Bacillariophyta", "Dinoflagellata_Syndiniales",
"Dinoflagellata_Dinophyceae", "Haptophyta", "Ciliophora", "Chlorophyta", "Picozoa", "Stramenopiles_X", "Radiolaria"),
selected = "Chlorophyta"),
radioButtons("taxlevel_iva", label = "Taxonomic level",
choices = c("Class"="Class", "Order"="Order", "Family"="Family", "Genus"="Genus", "OTU"="newOTUid_wgen")),
numericInput("numclust_iva", label = "# clusters", 2, min = 2, max = 20, step = NA, width = NULL),
downloadButton("downloadData_multipatt_043", "Download multipatt results 0.4-3"),
downloadButton("downloadData_multipatt_310", "Download multipatt results 3-10"),
downloadButton("downloadData_multipatt_1050", "Download multipatt results 10-50"),
downloadButton("downloadData_multipatt_50200", "Download multipatt results 50-200"),
downloadButton("downloadData_multipatt_3180", "Download multipatt results 3-180")
),
mainPanel(verbatimTextOutput(outputId = "iva"),
textOutput(outputId = "sjekk1"),
textOutput(outputId = "sjekk2"),
plotlyOutput(outputId = "mulpatplotly", height = 600, width = 1500),
plotlyOutput(outputId = "iva3.180", height = 600, width = 1500),
plotlyOutput(outputId = "iva3.10", height = 600, width = 1500),
plotlyOutput(outputId = "iva10.50", height = 600, width = 1500),
plotlyOutput(outputId = "iva50.200", height = 600, width = 1500)
)
)
)
)
#### Server ####
server <- function(input, output, session) {
#### Barplots, all fractions, with proportional abundance ####
prop_tax <- reactive({
taxlevel_barplot <- input$taxlevel #Select which taxonomic level to group by
otutab_sfsep <- otutab_sfsep %>% select(-three200.2) #Remove 'fake' size fraction 3-200
if (input$taxo_group2 != "All") {
otutab_tax0 <- otutab_sfsep %>% filter(Divisionlong %in% input$taxo_group2) #
} else {
otutab_tax0 <- otutab_sfsep
}
otutab_tax_num <- otutab_tax0 %>% select_if(is.numeric)
if (input$propchoice == "selectgroup") { #If "selectgroup" is selected, proportional abundance of "taxlevel" is calcualted based on only the selected groups
otutab_tax_selectprop <- sweep(otutab_tax_num, 2 , colSums(otutab_tax_num), FUN = "/")
} else {
otutab_tax_selectprop <- otutab_tax_num
}
otutab_tax_notnum <- otutab_tax0 %>% select_if(negate(is.numeric))
otutab_tax <- cbind.data.frame(otutab_tax_notnum, otutab_tax_selectprop)
otutab_tax[is.na(otutab_tax)] <- 0 #NA's because some samples have 0% of a certain class
otutab_tax$total <- otutab_tax %>% select_if(is.numeric) %>% rowSums()
taxlevtab <- otutab_tax %>% select(input$taxlevel) %>% droplevels.data.frame()
#Transform to presence-absence for OTU count per taxonomic group
otutab_sfsep_pa <- otutab_sfsep
otutab_sfsep_pa[otutab_sfsep_pa>0] <- 1
otutab_tax_pa0 <- otutab_tax0
otutab_tax_pa0[otutab_tax_pa0>0] <- 1
#Proportion of total or select, presence absence
otutab_tax_pa_num <- otutab_tax_pa0 %>% select_if(is.numeric)
if (input$propchoice == "selectgroup") {
otutab_tax_pa_selectprop <- sweep(otutab_tax_pa_num, 2 , colSums(otutab_tax_pa_num), FUN = "/")
} else {
otutab_tax_pa_selectprop <- otutab_tax_pa_num
}
sjekk_prop <- colSums(otutab_tax_pa_selectprop)[1]
otutab_tax_pa_notnum <- otutab_tax_pa0 %>% select_if(negate(is.numeric))
otutab_tax_pa <- cbind.data.frame(otutab_tax_pa_notnum, otutab_tax_pa_selectprop)
otutab_tax_pa[is.na(otutab_tax_pa)] <- 0 #NA's because some samples have 0% of a certain class
otutab_tax_pa$total <- otutab_tax_pa %>% select_if(is.numeric) %>% rowSums()
taxgroupspre <- select(otutab_tax, -total) %>% group_by_(input$taxlevel) %>% summarise_if(is.numeric, sum)
taxgroupspre_pa <- select(otutab_tax_pa, -total) %>% group_by_(input$taxlevel) %>% summarise_if(is.numeric, sum)
# Select only taxonomic groups that constitute >=5% of the reads in at least one sample, to limit number of categories in stacked barchart.
limfun <- function(x) {
ifelse(x>=0.05,1,0)
}
taxgroupspre_mat <- as.matrix(taxgroupspre[,-1, drop = FALSE])
taxgroupspre_bin <- apply(taxgroupspre_mat, 2, limfun)
if (is.vector(taxgroupspre_bin)) {
taxgroupspre_bin2 <- as.data.frame(as.list(taxgroupspre_bin))
} else {
taxgroupspre_bin2 <- as.data.frame(taxgroupspre_bin)}
taxgroupspre_bin_sums <- taxgroupspre_bin2 %>% select_if(is.numeric) %>% mutate(rowsumm = rowSums(.))
taxgroupspre_bin_sums$taxgroups <- as.vector(taxgroupspre[[taxlevel_barplot]])
taxgroupspre_bin_sums_yes <- filter(taxgroupspre_bin_sums, rowsumm >0) %>% select(taxgroups)
taxgroups_select <- taxgroupspre %>% filter(get(input$taxlevel) %in% taxgroupspre_bin_sums_yes$taxgroups)
taxgroups_other <- taxgroupspre %>% filter(!get(input$taxlevel) %in% taxgroupspre_bin_sums_yes$taxgroups)
taxgroups_pa_select <- taxgroupspre_pa %>% filter(get(input$taxlevel) %in% taxgroupspre_bin_sums_yes$taxgroups)
taxgroups_pa_other <- taxgroupspre_pa %>% filter(!get(input$taxlevel) %in% taxgroupspre_bin_sums_yes$taxgroups)
if (dim(taxgroupspre_bin_sums_yes)[1] < dim(taxgroupspre_bin_sums)[1]) {
Taxonomic_group <- c(taxgroupspre_bin_sums_yes$taxgroups,"Other")
othersum <- colSums(taxgroups_other[,-1])
} else {
Taxonomic_group <- c(taxgroupspre_bin_sums_yes$taxgroups)
othersum <- NULL
}
#sjekk_prop <- head(taxgroupspre_mat)[1,] ####14.03.2020
#if (input$propchoice == "selectgroup") {
taxgroups_select2 <- rbind(taxgroups_select[,-1],othersum)
# taxgroups_select2 <- sweep(taxgroups_select2, 2 , colSums(taxgroups_select2), FUN = "/")
# } else {
# taxgroups_select2 <- rbind(taxgroups_select[,-1],othersum)
# }
#
taxgroups_select3 <- cbind.data.frame(Taxonomic_group,taxgroups_select2)
taxgroups_select3t <- t(taxgroups_select3)
taxgroups_select3t2 <- taxgroups_select3t[-1,]
taxgroups_select3t3 <- apply(taxgroups_select3t2, 2, as.numeric)
taxgroups_select3tdf0 <- as.data.frame(taxgroups_select3t3)
names(taxgroups_select3tdf0) <- Taxonomic_group
total04.3 <- taxgroups_select3 %>% select(samples0.4.3) %>% mutate(rowsumm = rowSums(.))
total3.10 <- taxgroups_select3 %>% select(samples3.10) %>% mutate(rowsumm = rowSums(.))
total10.50 <- taxgroups_select3 %>% select(samples10.50) %>% mutate(rowsumm = rowSums(.))
total50.200<- taxgroups_select3 %>% select(samples50.200) %>% mutate(rowsumm = rowSums(.))
total3.180 <- taxgroups_select3 %>% select(samples3.180) %>% mutate(rowsumm = rowSums(.))
totaltotal04.3 <- otutab_sfsep %>% select(samples0.4.3) %>% mutate(rowsumm = rowSums(.))
totaltotal3.10 <- otutab_sfsep %>% select(samples3.10) %>% mutate(rowsumm = rowSums(.))
totaltotal10.50 <- otutab_sfsep %>% select(samples10.50) %>% mutate(rowsumm = rowSums(.))
totaltotal50.200<- otutab_sfsep %>% select(samples50.200) %>% mutate(rowsumm = rowSums(.))
totaltotal3.180 <- otutab_sfsep %>% select(samples3.180) %>% mutate(rowsumm = rowSums(.))
taxgroups_select3tdf <- rbind(taxgroups_select3tdf0, total04.3$rowsumm/sum(totaltotal04.3$rowsumm), total3.10$rowsumm/sum(totaltotal3.10$rowsumm), total10.50$rowsumm/sum(totaltotal10.50$rowsumm), total50.200$rowsumm/sum(totaltotal50.200$rowsumm), total3.180$rowsumm/sum(totaltotal3.180$rowsumm))
taxgroups_select3tdf$sf <- factor(c(rep("sf0.4.3", npico), rep("sf3.10", nthree10),
rep("sf10.50", nten50), rep("sf50.200", nfifty200), rep("sf3.180", nthree180), c("sf0.4.3", "sf3.10", "sf10.50", "sf50.200", "sf3.180")), ordered = T,
levels = c("sf0.4.3", "sf3.10", "sf3.180", "sf10.50", "sf50.200"))
taxgroups_select3tdf$stdep <- factor(c(as.character(StationDepthsfsep_tb), as.character(rep("total",5))), levels = c(levels(StationDepthsfsep_tb), "total"), ordered = T)
taxgroups_select3tdf$cruise <- factor(c(cruisesfsep_tb$cruise, rep("total",5)), ordered = T)
taxgroups_select3tdf_mp12 <- taxgroups_select3tdf %>% filter(cruise %in% c("MP1", "MP2"))
taxgroups_select3tdf_mp345 <- taxgroups_select3tdf %>% filter(cruise %in% c("MP3", "MP4", "MP5"))
taxgroups_select3tdf_total <- taxgroups_select3tdf %>% filter(cruise %in% c("total"))
taxgroups_select3tdf_mp12melt <- melt(taxgroups_select3tdf_mp12)
taxgroups_select3tdf_mp345melt <- melt(taxgroups_select3tdf_mp345)
taxgroups_select3tdf_totalmelt <- melt(taxgroups_select3tdf_total)
#Recode cruise names to more meaningful
taxgroups_select3tdf_mp12melt$cruise <- recode(taxgroups_select3tdf_mp12melt$cruise, "MP1" = "Jan", "MP2" = "March")
taxgroups_select3tdf_mp345melt$cruise <- recode(taxgroups_select3tdf_mp345melt$cruise, "MP3" = "May", "MP4" = "Aug", "MP5" = "Nov")
p12 <- ggplot(taxgroups_select3tdf_mp12melt, aes(x=reorder(stdep, desc(stdep)), y = value, fill = variable, text = sprintf("Taxon: %s<br>Sample: %s<br>Percent: %s ", variable, stdep, round(value*100,3))))+
labs(title = "Proportional read abundance")+
geom_bar(stat = "identity", position = "stack")+
facet_grid(rows = vars(cruise), cols = vars(sf), scale = "free_y", space = "free_y")+
theme(axis.title.y = element_blank(),
axis.title.x = element_blank())+
ylim(0,1.01)+
coord_flip()
p12
MP12plotly <- ggplotly(p12, tooltip = "text")
p345 <- ggplot(taxgroups_select3tdf_mp345melt, aes(x=reorder(stdep, desc(stdep)), y = value, fill = variable, text = sprintf("Taxon: %s<br>Sample: %s<br>Percent: %s ", variable, stdep, round(value*100,3))))+
geom_bar(stat = "identity", position = "stack")+
facet_grid(rows = vars(cruise), cols = vars(sf), scale = "free_y", space = "free_y")+
theme(axis.title.y = element_blank(),
axis.title.x = element_blank())+
ylim(0,1.01)+
coord_flip()
p345
MP345plotly <- ggplotly(p345, tooltip ="text")
ptotal <- ggplot(taxgroups_select3tdf_totalmelt, aes(x=reorder(stdep, desc(stdep)), y = value, fill = variable, text = sprintf("Taxon: %s<br>Sample: %s<br>Percent: %s ", variable, stdep, round(value*100,3))))+
geom_bar(stat = "identity", position = "stack")+
facet_grid(rows = vars(cruise), cols = vars(sf), scale = "free_y", space = "free_y")+
theme(axis.title.y = element_blank())+
ylim(0,1.01)+
coord_flip()
ptotal
totalplotly <- ggplotly(ptotal, tooltip = "text")
totalplotly
#### Barplot, proportional OTU richness ####
if (dim(taxgroupspre_bin_sums_yes)[1] < dim(taxgroupspre_bin_sums)[1]) {
Taxonomic_group <- c(taxgroupspre_bin_sums_yes$taxgroups,"Other")
othersum_pa <- colSums(taxgroups_pa_other[,-1])
} else {
Taxonomic_group <- c(taxgroupspre_bin_sums_yes$taxgroups)
othersum_pa <- NULL
}
taxgroups_pa_select2 <- rbind(taxgroups_pa_select[,-1],othersum_pa)
#otutab_sfsep_pa_num <- otutab_sfsep_pa %>% select_if(is.numeric)
#otutab_sfsep_pa_num_prop <- sweep(otutab_sfsep_pa_num, 2 , colSums(otutab_sfsep_pa_num), FUN = "/")
otutab_sfsep_pa_num_prop <- otutab_tax_pa_selectprop
#taxgroups_pa_select2_prop <- sweep(taxgroups_pa_select2, 2, colSums(otutab_sfsep_pa_num), FUN = "/")
taxgroups_pa_select2_prop <- taxgroups_pa_select2
# taxgroups_pa_select2_prop <- sweep(taxgroups_pa_select2, 2, colSums(taxgroups_pa_select2), FUN = "/")
taxgroups_pa_select3 <- cbind.data.frame(Taxonomic_group,taxgroups_pa_select2_prop)
taxgroups_pa_select3t <- t(taxgroups_pa_select3)
taxgroups_pa_select3t2 <- taxgroups_pa_select3t[-1,]
taxgroups_pa_select3t3 <- apply(taxgroups_pa_select3t2, 2, as.numeric)
taxgroups_pa_select3tdf0 <- as.data.frame(taxgroups_pa_select3t3)
names(taxgroups_pa_select3tdf0) <- Taxonomic_group
patotal04.3 <- taxgroups_pa_select3 %>% select(samples0.4.3) %>% mutate(rowsumm = rowSums(.))
patotal3.10 <- taxgroups_pa_select3 %>% select(samples3.10) %>% mutate(rowsumm = rowSums(.))
patotal10.50 <- taxgroups_pa_select3 %>% select(samples10.50) %>% mutate(rowsumm = rowSums(.))
patotal50.200<- taxgroups_pa_select3 %>% select(samples50.200) %>% mutate(rowsumm = rowSums(.))
patotal3.180 <- taxgroups_pa_select3 %>% select(samples3.180) %>% mutate(rowsumm = rowSums(.))
patotaltotal04.3 <- otutab_sfsep_pa_num_prop %>% select(samples0.4.3) %>% mutate(rowsumm = rowSums(.))
patotaltotal3.10 <- otutab_sfsep_pa_num_prop %>% select(samples3.10) %>% mutate(rowsumm = rowSums(.))
patotaltotal10.50 <- otutab_sfsep_pa_num_prop %>% select(samples10.50) %>% mutate(rowsumm = rowSums(.))
patotaltotal50.200<- otutab_sfsep_pa_num_prop %>% select(samples50.200) %>% mutate(rowsumm = rowSums(.))
patotaltotal3.180 <- otutab_sfsep_pa_num_prop %>% select(samples3.180) %>% mutate(rowsumm = rowSums(.))
taxgroups_pa_select3tdf <- rbind(taxgroups_pa_select3tdf0, patotal04.3$rowsumm/sum(patotaltotal04.3$rowsumm), patotal3.10$rowsumm/sum(patotaltotal3.10$rowsumm), patotal10.50$rowsumm/sum(patotaltotal10.50$rowsumm), patotal50.200$rowsumm/sum(patotaltotal50.200$rowsumm), patotal3.180$rowsumm/sum(patotaltotal3.180$rowsumm))
taxgroups_pa_select3tdf$sf <- factor(c(rep("sf0.4.3", npico), rep("sf3.10", nthree10),
rep("sf10.50", nten50), rep("sf50.200", nfifty200), rep("sf3.180", nthree180), c("sf0.4.3", "sf3.10", "sf10.50", "sf50.200", "sf3.180")), ordered = T,
levels = c("sf0.4.3", "sf3.10", "sf3.180", "sf10.50", "sf50.200"))
taxgroups_pa_select3tdf$stdep <- factor(c(as.character(StationDepthsfsep_tb), as.character(rep("total",5))), levels = c(levels(StationDepthsfsep_tb), "total"), ordered = T)
taxgroups_pa_select3tdf$cruise <- factor(c(cruisesfsep_tb$cruise, rep("total",5)), ordered = T)
taxgroups_pa_select3tdf_mp12 <- taxgroups_pa_select3tdf %>% filter(cruise %in% c("MP1", "MP2"))
taxgroups_pa_select3tdf_mp345 <- taxgroups_pa_select3tdf %>% filter(cruise %in% c("MP3", "MP4", "MP5"))
taxgroups_pa_select3tdf_mp12melt <- melt(taxgroups_pa_select3tdf_mp12)
taxgroups_pa_select3tdf_mp345melt <- melt(taxgroups_pa_select3tdf_mp345)
taxgroups_pa_select3tdf_total <- taxgroups_pa_select3tdf %>% filter(cruise %in% c("total"))
taxgroups_pa_select3tdf_totalmelt <- melt(taxgroups_pa_select3tdf_total)
p12_pa <- ggplot(taxgroups_pa_select3tdf_mp12melt, aes(x=reorder(stdep, desc(stdep)), y = value, fill = variable, text = sprintf("Taxon: %s<br>Sample: %s<br>Percent: %s ", variable, stdep, round(value*100,3))))+
labs(title = "Proportional OTU richness")+
geom_bar(stat = "identity", position = "stack")+
facet_grid(rows = vars(cruise), cols = vars(sf), scale = "free_y", space = "free_y")+
theme(axis.title.y = element_blank(),
axis.title.x = element_blank())+
ylim(0,1.01)+
coord_flip()
p12_pa
MP12plotly_pa <- ggplotly(p12_pa, tooltip = "text")
p345_pa <- ggplot(taxgroups_pa_select3tdf_mp345melt, aes(x=reorder(stdep, desc(stdep)), y = value, fill = variable, text = sprintf("Taxon: %s<br>Sample: %s<br>Percent: %s ", variable, stdep, round(value*100,3))))+
geom_bar(stat = "identity", position = "stack")+
facet_grid(rows = vars(cruise), cols = vars(sf), scale = "free_y", space = "free_y")+
theme(axis.title.y = element_blank(),
axis.title.x = element_blank())+
ylim(0,1.01)+
coord_flip()
p345_pa
MP345plotly_pa <- ggplotly(p345_pa, tooltip ="text")
ptotal_pa <- ggplot(taxgroups_pa_select3tdf_totalmelt, aes(x=reorder(stdep, desc(stdep)), y = value, fill = variable, text = sprintf("Taxon: %s<br>Sample: %s<br>Percent: %s ", variable, stdep, round(value*100,3))))+
labs(title = "Proportional OTU richness")+
geom_bar(stat = "identity", position = "stack")+
facet_grid(rows = vars(cruise), cols = vars(sf), scale = "free_y", space = "free_y")+
theme(axis.title.y = element_blank(),
axis.title.x = element_blank())+
# ylim(0,1)+
coord_flip()
ptotal_pa
totalplotly_pa <- ggplotly(ptotal_pa, tooltip = "text")
totalplotly_pa
list(MP12plotly = MP12plotly, MP345plotly = MP345plotly, totalplotly = totalplotly, MP12plotly_pa = MP12plotly_pa, MP345plotly_pa = MP345plotly_pa, totalplotly_pa =totalplotly_pa, sjekk_prop = sjekk_prop)
})
#### Alpha diversity - Richness, Shannon index, evenness ####
alpha_div <- reactive({
if (input$taxo_group_adiv != "All") {
otutab_tax <- otutab_sfsep %>% filter(Divisionlong %in% input$taxo_group_adiv)
} else {
otutab_tax <- otutab_sfsep
}
richtab <- otutab_tax %>% select_if(is.numeric)
richtab_t <- t(richtab)
richtab_tdf <- as.data.frame(richtab_t)
richtab_tdf$richness <- specnumber(richtab_t)
richtab_tdf$H <- diversity(richtab_t) #Shannon entropy
richtab_tdf$N1 <- exp(richtab_tdf$H) #Shannon diversity
richtab_tdf$N2 <- diversity(richtab_t, "inv")
richtab_tdf$E10 <- richtab_tdf$N1/richtab_tdf$richness
richtab_tdf$E20 <- richtab_tdf$N2/richtab_tdf$richness
# Calculate percent of maximum richness
percofmax <- c(NULL)
percofmax[c(1:npico)] <- 100 * richtab_tdf$richness[c(1:npico)]/max(richtab_tdf$richness[c(1:npico)])
percofmax[c(45:70)] <- 100 * richtab_tdf$richness[c(45:70)]/max(richtab_tdf$richness[c(45:70)])
percofmax[c(71:96)] <- 100 * richtab_tdf$richness[c(71:96)]/max(richtab_tdf$richness[c(71:96)])
percofmax[c(97:122)] <- 100 * richtab_tdf$richness[c(97:122)]/max(richtab_tdf$richness[c(97:122)])
percofmax[c(123:140)] <- 100 * richtab_tdf$richness[c(123:140)]/max(richtab_tdf$richness[c(123:140)])
richtab_tdf$percofmax <- round(percofmax,0)
#Create table for ggplot
richtab_tdf$sample <- names(richtab)
richtab_tdf$sf <- c(rep("sf0.4.3", npico), rep("sf3.10", nthree10),
rep("sf10.50", nten50), rep("sf50.200", nfifty200), rep("sf3.180", nthree180))
richtab_tdf <- full_join(richtab_tdf, depthbinsfsep_tb, by = "sample")
richtab_tdf$sf <- factor(richtab_tdf$sf, levels = c("sf0.4.3", "sf3.10", "sf10.50", "sf50.200", "sf3.180"), ordered =T)
richtab_tdf$stdep <- StationDepthsfsep_tb
richtab_tdf$cruise <- factor(cruisesfsep_tb$cruise, ordered = T)
richtab_tdfforplot <- richtab_tdf %>% select(richness, H, N1, N2, E10, E20, sample, sf, depthbin, stdep, cruise, percofmax)
#whichvar <- "N1"
richtab_tdf_12 <- richtab_tdfforplot %>% filter(cruise %in% c("MP1", "MP2"))
richtab_tdf_345 <- richtab_tdfforplot %>% filter(cruise %in% c("MP3", "MP4", "MP5"))
rich12 <- ggplot(richtab_tdf_12, aes(x=cruise, y = richness, colour = depthbin, text = sprintf("Richness: %s<br>Sample: %s<br>Percent of max: %s ", richness, stdep, percofmax)))+
geom_point( size = bpps)+
facet_grid(rows = vars(sf))+
ylim(0,2200)
rich12
rich12ly <- ggplotly(rich12, tooltip = "text")
rich12ly
rich345 <- ggplot(richtab_tdf_345, aes(x=cruise, y = richness, colour = depthbin, text = sprintf("Richness: %s<br>Sample: %s<br>Percent of max: %s ", richness, stdep, percofmax)))+
geom_point(size = bpps)+
facet_grid(rows = vars(sf))+
ylim(0,2200)
rich345
rich345ly <- ggplotly(rich345, tooltip = "text")
rich345ly
even12 <- ggplot(richtab_tdf_12, aes(x=cruise, y = E10, colour = depthbin, text = sprintf("Evenness: %s<br>Sample: %s ", round(E10,2), stdep)))+
geom_point(size = bpps)+
facet_grid(rows = vars(sf))
even12ly <- ggplotly(even12, tooltip = "text")
even12ly
even345 <- ggplot(richtab_tdf_345, aes(x=cruise, y = E10, colour = depthbin, text = sprintf("Evenness: %s<br>Sample: %s ", E10, stdep)))+
geom_point(size = bpps)+
facet_grid(rows = vars(sf))
even345ly <- ggplotly(even345, tooltip = "text")
even345ly
shan12 <- ggplot(richtab_tdf_12, aes(x=cruise, y = N1, colour = depthbin, text = sprintf("Evenness: %s<br>Sample: %s ", round(E10,2), stdep)))+
geom_point(size = bpps)+
facet_grid(rows = vars(sf))
shan12ly <- ggplotly(shan12, tooltip = "text")
shan12ly
shan345 <- ggplot(richtab_tdf_345, aes(x=cruise, y = N1, colour = depthbin, text = sprintf("Evenness: %s<br>Sample: %s ", E10, stdep)))+
geom_point(size = bpps)+
facet_grid(rows = vars(sf))
shan345ly <- ggplotly(shan345, tooltip = "text")
shan345ly
list(rich12ly = rich12ly, rich345ly = rich345ly, even12ly = even12ly, even345ly = even345ly, shan12ly = shan12ly, shan345ly = shan345ly)
})
#### Chloroplast data ####
prop_tax_chl <- reactive({
taxlev_chl <- input$taxlevel_chl
if (input$taxo_group3 != "All") {
otutab0_tax_chl <- chltab %>% filter(Division %in% input$taxo_group3)
} else {
otutab0_tax_chl <- chltab
}
if (is.vector(otutab0_tax_chl)) {
otutab_tax_chl <- as.data.frame(as.list(otutab0_tax_chl))
} else {
otutab_tax_chl <- otutab0_tax_chl}
tabtest <- head(otutab_tax_chl)
otutab_tax_chl$total <- otutab_tax_chl %>% select_if(is.numeric) %>% rowSums()
taxlevtab_chl <- otutab_tax_chl %>% select(input$taxlevel_chl) %>% droplevels.data.frame()
taxgroupspre_chl <- select(otutab_tax_chl, -total) %>% group_by_(input$taxlevel_chl) %>% summarise_if(is.numeric, sum)
limfun_chl <- function(x) {
ifelse(x>=0.0005,1,0)
}
taxgroupspre_mat_chl <- as.matrix(taxgroupspre_chl[,-1, drop = FALSE])
taxgroupspre_bin_chl <- apply(taxgroupspre_mat_chl, 2, limfun_chl)
if (is.vector(taxgroupspre_bin_chl)) {
taxgroupspre_bin2_chl <- as.data.frame(as.list(taxgroupspre_bin_chl))
} else {
taxgroupspre_bin2_chl <- as.data.frame(taxgroupspre_bin_chl)}
taxgroupspre_bin_sums_chl <- taxgroupspre_bin2_chl %>% select_if(is.numeric) %>% mutate(rowsumm = rowSums(.))
taxgroupspre_bin_sums_chl$taxgroups <- as.vector(taxgroupspre_chl[[taxlev_chl]])
taxgroupspre_bin_sums_yes_chl <- filter(taxgroupspre_bin_sums_chl, rowsumm >0) %>% select(taxgroups)
taxgroups_select_chl <- taxgroupspre_chl %>% filter(get(input$taxlevel_chl) %in% taxgroupspre_bin_sums_yes_chl$taxgroups)
taxgroups_other_chl <- taxgroupspre_chl %>% filter(!get(input$taxlevel_chl) %in% taxgroupspre_bin_sums_yes_chl$taxgroups)
if (dim(taxgroupspre_bin_sums_yes_chl)[1] < dim(taxgroupspre_bin_sums_chl)[1]) {
Taxonomic_group_chl <- c(taxgroupspre_bin_sums_yes_chl$taxgroups,"Other")
othersum_chl <- colSums(taxgroups_other_chl[,-1])
} else {
Taxonomic_group_chl <- c(taxgroupspre_bin_sums_yes_chl$taxgroups)
othersum_chl <- NULL
}
#
#
#
taxgroups_select2_chl <- rbind(taxgroups_select_chl[,-1],othersum_chl)
#
#
#
taxgroups_select3_chl <- cbind.data.frame(Taxonomic_group_chl,taxgroups_select2_chl)
#
#
taxgroups_select3t_chl <- t(taxgroups_select3_chl)
taxgroups_select3t2_chl <- taxgroups_select3t_chl[-1,]
taxgroups_select3t3_chl <- apply(taxgroups_select3t2_chl, 2, as.numeric)
taxgroups_select3tdf_chl <- as.data.frame(taxgroups_select3t3_chl)
names(taxgroups_select3tdf_chl) <- Taxonomic_group_chl
#
#
taxgroups_select3tdf_chl$stdep <- StationDepths_tb_chl
taxgroups_select3tdf_chl$cruise <- factor(chlmeta$cruise, ordered = T, levels = c("Jan", "Mar", "May", "Aug", "Nov"))
taxgroups_select3tdf_chl$nuclac <- factor(chlmeta$nuclac)
#
taxgroups_select3tdf_melt_chl <- melt(taxgroups_select3tdf_chl)
#
#
chloroplot <- ggplot(taxgroups_select3tdf_melt_chl, aes(x=reorder(stdep, desc(stdep)), y = value, fill = variable, text = sprintf("Taxon: %s<br>Sample: %s<br>Percent: %s ", variable, stdep, round(value*100,3))))+
geom_bar(stat = "identity", position = "stack")+
facet_grid(rows = vars(cruise), cols = vars(nuclac), scale = "free_y", space = "free_y")+
theme(axis.title.y = element_blank())+
coord_flip()
#
#
chloroplotly <- ggplotly(chloroplot, tooltip = "text")
#
#
list(tabtest = tabtest, chloroplotly = chloroplotly)
})
#### NMDS + clustering ####
beta_div_nmds <- reactive({
# Get variables from input
sf <- sfnames[[input$sizefract1]] #Size fraction of interest
taxogroup_clust <- input$taxo_group_clust #Taxonomic group of interest
taxlevel <- input$taxlevel_clustplot #Taxonomic level of grouping for barplot
#select sample_sf, and the corresponding observations of environmental variables in meta_table
if (input$sizefract1 == "sf3.200") {
otutab_sf <- otutab_sfsep[c(three200, "Divisionlong", "newOTUid_wgen")]
sampnames_meta_sf <- meta_table["sf0.4.3"]
meta_sf_all <- meta_table[match(sfnames[["sf0.4.3"]],as.character(sampnames_meta_sf[,1])),]
} else {
otutab_sf <- otutab_sfsep[c(as.character(sf), "Divisionlong", "newOTUid_wgen")]
sampnames_meta_sf <- meta_table[input$sizefract1]
meta_sf_all <- meta_table[match(colnames(otutab_sf %>% select(-Divisionlong, -newOTUid_wgen)),as.character(sampnames_meta_sf[,1])),]
}
meta_sf <- droplevels(meta_sf_all)
if (input$taxo_group_clust != "All") {
otutab_sf_tax <- otutab_sf %>% filter(Divisionlong %in% taxogroup_clust)
} else {
otutab_sf_tax <- otutab_sf
}
otutab_sf_tax_num <- otutab_sf_tax %>% select(-Divisionlong, -newOTUid_wgen)
numotus <- length(which(rowSums(otutab_sf_tax_num)>0))
rownames(otutab_sf_tax) <- otutab_sf_tax$newOTUid_wgen
#### Run NMDS + cluster ####
nmds_tax <- mp_nmds(otutab_sf_tax %>% select(-Divisionlong, -newOTUid_wgen)) #function mp_nmds transposes otu table
nmds_table <- data.frame(cbind(nmds_tax$points[,1], nmds_tax$points[,2], meta_sf))
names(nmds_table)[c(1,2)] <- c("nmds_axis1", "nmds_axis2")
###Plotting NMDS
plot_nmds <- nmdsplot(nmds_table)+
xlim(min(nmds_table$nmds_axis1)-.1,max(nmds_table$nmds_axis1)+.1)+
ylim(min(nmds_table$nmds_axis2)-.1,max(nmds_table$nmds_axis2)+.1)#+
#geom_path(aes(x = V1, y = V2), data = ordih1)
#coord_cartesian(xlim = ranges2$x, ylim = ranges2$y, expand = FALSE)
nmdsplotly <- ggplotly(plot_nmds, tooltip = "text")
#Hierarchical clustering of samples
numclus <- input$clusters
otustand <- decostand(t(otutab_sf_tax_num), method = "hellinger")
otudist <- vegdist(otustand, distance = "jaccard")
otuclust0 <- hclust(otudist, "average")
library(cluster)
#k <- 4
clust <- cutree(otuclust0,k=numclus) # k clusters
dendr <- dendro_data(otuclust0, type="rectangle") # convert for ggplot
clust.df <- data.frame(label=rownames(t(otutab_sf_tax_num)), cluster=factor(clust))
dendr[["labels"]] <- merge(dendr[["labels"]],clust.df, by="label")
rect <- aggregate(x~cluster,label(dendr),range)
rect <- data.frame(rect$cluster,rect$x)
ymax <- mean(otuclust0$height[length(otuclust0$height)-((numclus-2):(numclus-1))])
dendroplot <- ggplot() +
geom_segment(data=segment(dendr), aes(x=x, y=y, xend=xend, yend=yend)) +
geom_text(data=label(dendr), aes(x, y, label=label, hjust=0, color=cluster),
size=3) +
geom_rect(data=rect, aes(xmin=X1-.3, xmax=X2+.3, ymin=0, ymax=ymax),
color="red", fill=NA)+
#geom_hline(yintercept=0.33, color="blue")+
coord_flip() + scale_y_reverse(expand=c(0.4, 0)) +
theme(legend.position = "none") +
theme_dendro()
#clustplotly <- ggplotly(clustplot) #here it goes wrong
#https://stackoverflow.com/questions/24140339/tree-cut-and-rectangles-around-clusters-for-a-horizontal-dendrogram-in-r
#### end cluster ####
grp <- cutree(otuclust0, k =input$clusters)
##### barplot for cluster #####
if (input$taxo_group_clust != "All") {
otutab_tax <- otutab_sfsep %>% filter(Divisionlong %in% input$taxo_group_clust)
otutab_tax2 <- otutab_sfsep %>% filter(Divisionlong %in% input$taxo_group_clust)
} else {
otutab_tax <- otutab_sfsep
otutab_tax2 <- otutab_sfsep
}
if (input$sizefract1 == "sf3.200") {
otutab_tax_sf0 <- otutab_tax2[, three200]
} else {
otutab_tax_sf0 <- otutab_tax[,c(as.character(sf))]
}
if (input$propchoice == "selectgroup") {
otutab_tax_sf1 <- sweep(otutab_tax_sf0, 2 , colSums(otutab_tax_sf0), FUN = "/")
} else {
otutab_tax_sf1 <- otutab_tax_sf0
}