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+Sample BarcodeSequence LinkerPrimerSequence IDTplateposition SampleNo Project spatialtree transecttree Vertposition litterorother BlockNo grndcnpy samplelocation sample_type Description +230 TTCTCCATCACA TTCTCCATCACACCGTAAAACGACGGCCAG C4 230 transects NA 6 5 abovelitter NA NA NA NA transects +225 CATACCGTGAGT CATACCGTGAGTCCGTAAAACGACGGCCAG E2 225 transects NA 5 5 abovelitter NA NA NA NA transects +212 CATTCGTGGCGT CATTCGTGGCGTCCGTAAAACGACGGCCAG G4 212 transects NA 3 2 abovelitter NA NA NA NA transects +231 CGATAGGCCTTA CGATAGGCCTTACCCGTAAAACGACGGCCAG C5 231 transects NA 7 1 litter NA NA NA NA transects +241 CTATCATCCTCA CTATCATCCTCACCCGTAAAACGACGGCCAG D5 241 transects NA 9 1 litter NA NA NA NA transects +232 GAAACATCCCAC GAAACATCCCACCCGTAAAACGACGGCCAG C6 232 transects NA 7 2 abovelitter NA NA NA NA transects +216 CGCTGTGGATTA CGCTGTGGATTACCGTAAAACGACGGCCAG B1 216 transects NA 4 1 litter NA NA NA NA transects +206 CACTCATCATTC CACTCATCATTCCCGTAAAACGACGGCCAG A1 206 transects NA 2 1 litter NA NA NA NA transects +211 ATTCGGTAGTGC ATTCGGTAGTGCCCGTAAAACGACGGCCAG A6 211 transects NA 3 1 litter NA NA NA NA transects +238 GCATCAGAGTTA GCATCAGAGTTACCCCGTAAAACGACGGCCAG D2 238 transects NA 8 3 abovelitter NA NA NA NA transects +239 TTAAGACAGTCG TTAAGACAGTCGCCGTAAAACGACGGCCAG D3 239 transects NA 8 4 abovelitter NA NA NA NA transects +233 ACAACACTCCGA ACAACACTCCGACCGTAAAACGACGGCCAG C7 233 transects NA 7 3 abovelitter NA NA NA NA transects +243 ACGGGTCATCAT ACGGGTCATCATCCGTAAAACGACGGCCAG D7 243 transects NA 9 3 abovelitter NA NA NA NA transects +244 GAGAGCAACAGA GAGAGCAACAGACCGTAAAACGACGGCCAG D8 244 transects NA 9 4 abovelitter NA NA NA NA transects +223 TCAGTTCTCGTT TCAGTTCTCGTTCCGTAAAACGACGGCCAG B8 223 transects NA 5 3 abovelitter NA NA NA NA transects +207 ACACGCGGTTTA ACACGCGGTTTACCGTAAAACGACGGCCAG A2 207 transects NA 2 2 abovelitter NA NA NA NA transects +240 GTTCGGTGTCCA GTTCGGTGTCCACCCGTAAAACGACGGCCAG D4 240 transects NA 8 5 abovelitter NA NA NA NA transects +245 AGCTGCACCTAA AGCTGCACCTAACGTAAAACGACGGCCAG E4 245 transects NA 9 5 abovelitter NA NA NA NA transects +220 ATTCTCTCACGT ATTCTCTCACGTCCCGTAAAACGACGGCCAG B5 220 transects NA 4 5 abovelitter NA NA NA NA transects +248 CGCAGATTAGTA CGCAGATTAGTACCGTAAAACGACGGCCAG F2 248 transects NA 10 3 abovelitter NA NA NA NA transects +249 TTCCTAGGCCAG TTCCTAGGCCAGCCCGTAAAACGACGGCCAG F3 249 transects NA 10 4 abovelitter NA NA NA NA transects +210 TTCGATGCCGCA TTCGATGCCGCACCCGTAAAACGACGGCCAG A5 210 transects NA 2 5 abovelitter NA NA NA NA transects +250 GTCTTCAGCAAG GTCTTCAGCAAGCGTAAAACGACGGCCAG F4 250 transects NA 10 5 abovelitter NA NA NA NA transects +203 GTCGAATTTGCG GTCGAATTTGCGCCCGTAAAACGACGGCCAG G3 203 transects NA 1 3 abovelitter NA NA NA NA transects +208 TCTGCACTGAGC TCTGCACTGAGCCCGTAAAACGACGGCCAG A3 208 transects NA 2 3 abovelitter NA NA NA NA transects +215 CGACTCTAAACG CGACTCTAAACGCCGTAAAACGACGGCCAG E5 215 transects NA 3 5 abovelitter NA NA NA NA transects +234 CCTTGACCGATG CCTTGACCGATGCCGTAAAACGACGGCCAG C8 234 transects NA 7 4 abovelitter NA NA NA NA transects +219 CTCCCTTTGTGT CTCCCTTTGTGTCCGTAAAACGACGGCCAG B4 219 transects NA 4 4 abovelitter NA NA NA NA transects +226 TACCGAAGGTAT TACCGAAGGTATCCGTAAAACGACGGCCAG E6 226 transects NA 6 1 litter NA NA NA NA transects +236 CGTGCTTAGGCT CGTGCTTAGGCTCCGTAAAACGACGGCCAG E7 236 transects NA 8 1 litter NA NA NA NA transects +237 AGCTGTCAAGCT AGCTGTCAAGCTCCGTAAAACGACGGCCAG D1 237 transects NA 8 2 abovelitter NA NA NA NA transects +213 GTAAATTCAGGC GTAAATTCAGGCCCGTAAAACGACGGCCAG A8 213 transects NA 3 3 abovelitter NA NA NA NA transects +218 ACGATTCGAGTC ACGATTCGAGTCCCGTAAAACGACGGCCAG B3 218 transects NA 4 3 abovelitter NA NA NA NA transects +214 TTCTCTCGACAT TTCTCTCGACATCCGTAAAACGACGGCCAG E1 214 transects NA 3 4 abovelitter NA NA NA NA transects +229 GTGCAACCAATC GTGCAACCAATCCCGTAAAACGACGGCCAG C3 229 transects NA 6 4 abovelitter NA NA NA NA transects +246 CGCTCACAGAAT CGCTCACAGAATCGTAAAACGACGGCCAG E8 246 transects NA 10 1 litter NA NA NA NA transects +242 GTATTTCGGACG GTATTTCGGACGCCGTAAAACGACGGCCAG D6 242 transects NA 9 2 abovelitter NA NA NA NA transects +221 CGAGCTGTTACC CGAGCTGTTACCCCCGTAAAACGACGGCCAG B6 221 transects NA 5 1 litter NA NA NA NA transects +228 AGTTGTAGTCCG AGTTGTAGTCCGCCGTAAAACGACGGCCAG C2 228 transects NA 6 3 abovelitter NA NA NA NA transects +227 ATGTGTGTAGAC ATGTGTGTAGACCCGTAAAACGACGGCCAG C1 227 transects NA 6 2 abovelitter NA NA NA NA transects +222 TATCTATCCTGC TATCTATCCTGCCCGTAAAACGACGGCCAG B7 222 transects NA 5 2 abovelitter NA NA NA NA transects +201 AGTAGCGGAAGA AGTAGCGGAAGACCGTAAAACGACGGCCAG G1 201 transects NA 1 1 litter NA NA NA NA transects +202 CGTACTCTCGAG CGTACTCTCGAGCCGTAAAACGACGGCCAG G2 202 transects NA 1 2 abovelitter NA NA NA NA transects +204 ACTCTAGCCGGT ACTCTAGCCGGTCCCCGTAAAACGACGGCCAG G4 204 transects NA 1 4 abovelitter NA NA NA NA transects +205 TACTCGGGAACT TACTCGGGAACTCCGTAAAACGACGGCCAG G5 205 transects NA 1 5 abovelitter NA NA NA NA transects +217 CCTGCGAAGTAT CCTGCGAAGTATCCGTAAAACGACGGCCAG B2 217 transects NA 4 2 abovelitter NA NA NA NA transects +235 TTGCCAAGAGTC TTGCCAAGAGTCCCGTAAAACGACGGCCAG E3 235 transects NA 7 5 abovelitter NA NA NA NA transects +209 ACCGTGCTCACA ACCGTGCTCACACCCGTAAAACGACGGCCAG A4 209 transects NA 2 4 abovelitter NA NA NA NA transects +247 GCTTGAGCTTGA GCTTGAGCTTGACCGTAAAACGACGGCCAG F1 247 transects NA 10 2 abovelitter NA NA NA NA transects diff --git a/Data_Files/otu.tsv b/Data_Files/otu.tsv new file mode 100644 index 0000000..bd428f1 --- /dev/null +++ b/Data_Files/otu.tsv @@ -0,0 +1,72799 @@ +230 225 212 231 241 232 216 206 211 238 239 233 243 244 223 207 240 245 220 248 249 210 250 203 208 215 234 219 226 236 237 213 218 214 229 246 242 221 228 227 222 201 202 204 205 217 235 209 247 +1 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 1 0 0 0 0 0 0 0 0 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diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..3877ae0 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md index 48d309d..34b9e59 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,19 @@ -# How to use this repo - -This repo should include the source code for all visualizations presented on the SUS website. - -To add a visualization: - -1. Fork this repository into your own github. -2. Create a new branch with the name of your viz or analysis. -3. Add your working code and commit. -4. Submit a Pull Request back to the speeding-up-science-workshops/speeding-up-science-code repository. Your code will be added shortly. -5. Visit https://speeding-up-science-workshops.github.io/editing_this_site/ to update the website! - -Contact us via slack or email if you have questions! +# Speeding up Science Metabarcoding + +This repository contains code for visualizing an interactive phylogenetic tree + +The code can be executed by clicking on the binder link. You can also upload your own data to binder, so you use our code for yourself, without installing any of the needed software. All you need is a web browser. Note that any changes you make will not be saved, so if you want to keep anything, so be sure to download any changes you make. + +RStudio: [![Binder](http://mybinder.org/badge_logo.svg)](http://mybinder.org/v2/gh/devonorourke/sus19mb/Niel?urlpath=rstudio) + + +This visualizations is presented as a R scripts so when you open the binder, it will look like RStudio. The script you want can be opened from the files tab in the lower right of RStudio. + + +## Interactive Tree + +![tree](tree.png) + +See the taxonomic classification of the leaf on mouse over. + +```interactive_tree.Rmd``` diff --git a/binder/environment.yml b/binder/environment.yml new file mode 100644 index 0000000..7df649b --- /dev/null +++ b/binder/environment.yml @@ -0,0 +1,4 @@ +channels: + - conda-forge +dependencies: + - notedown diff --git a/binder/install.R b/binder/install.R new file mode 100644 index 0000000..1d44011 --- /dev/null +++ b/binder/install.R @@ -0,0 +1,5 @@ +pkgs = c("reshape2", "dplyr", "readr", "ggplot2", "plotly","upsetr","ggpubr","knitr") +ncores = parallel::detectCores() +install.packages(pkgs, Ncpus = ncores) +source('http://bioconductor.org/biocLite.R') +biocLite('phyloseq') \ No newline at end of file diff --git a/binder/runtime.txt b/binder/runtime.txt new file mode 100644 index 0000000..722d099 --- /dev/null +++ b/binder/runtime.txt @@ -0,0 +1 @@ +r-2019-04-10 diff --git a/export_to_emperor.R b/export_to_emperor.R new file mode 100644 index 0000000..c877e0f --- /dev/null +++ b/export_to_emperor.R @@ -0,0 +1,67 @@ +# Script to make the files needed for emperor +# from a phyloseq object + +library(phyloseq) +library(tidyverse) + + +# Number of principle components to use +numPC <- 5 + +# Read phyloseq object +ps <- readRDS('../Downloads/Phyloseq_filtered.rds') + + +# Do MDS calculations +pslog <- transform_sample_counts(ps, function(x) log(1 + x)) +out.bc.log <- ordinate(pslog, method = "MDS", distance = "bray") + + +a <- out.bc.log$vectors +a <- as.data.frame(a) +a <- a[,1:numPC] +a$pc <- row.names(a) +a <- as_tibble(a) +a <- select(a, pc, everything()) +#names(a) +names(a)[1] <- 'pc vector number' + + +# Write PCA data to file +write.table(a, 'emperor/pca.dat', quote=F, sep="\t", row.names = F) + + +# Prep eigenvalue and percent explained data +b <- as.data.frame(out.bc.log$values) + +eig <- b$Eigenvalues +eig <- eig[1:numPC] +eig <- c('eigvals',eig) + +varexp <- b$Relative_eig +varexp <- 100 * varexp +varexp <- varexp[1:numPC] +varexp <- c('% variation explained', varexp) + + + +# Add that to PCA data file +cat('\n\n', + paste0(eig, collapse='\t'), '\n', + paste0(varexp, collapse='\t'), + file='emperor/pca.dat','\n', append=T) + +# Do meta data file + +meta <- sample_data(ps) +meta <- as_tibble(meta) +meta <- rename(meta, '#SampleID'=SampleID) +meta <- dplyr::select(meta, `#SampleID`, everything()) + +write_tsv(meta, 'emperor/meta.txt') + + +# On the command line, +# conda activate emperor +# make_emperor.py -i pca.dat -m meta.dat -o outdir +# conda deactivate diff --git a/interactive_tree.Rmd b/interactive_tree.Rmd new file mode 100644 index 0000000..723cfd1 --- /dev/null +++ b/interactive_tree.Rmd @@ -0,0 +1,270 @@ +--- +title: "Interactive Tree" +author: "Niel Infante" +output: html_document +--- + +This document will take you through creating an interactive tree plot of your microbiome data. + + + +```{r setup, include=F} +knitr::opts_chunk$set(echo = T, message=F, warning=F) + +``` +### Load the needed packages. + +```{r packages} +library(phyloseq) +library(tidyverse) +library(plotly) +library(data.table) +library(scales) + +``` + +### Read the needed data, turn it into a phyloseq object + +Some data munging needs to happen to get the data files we have into the form needed for phyloseq. Ignore these steps if your data is well formed. + +```{r read_data} + +map <- read_csv('Data_Files/bact_alldata_mapfile.csv') +tax <- read_csv('Data_Files/bact_alldata_taxatable_wTax.csv') + +otu <- as.data.frame(select(tax, -X1,-taxonomy)) +row.names(otu) <- tax$X1 +otu <- otu_table(otu, taxa_are_rows = T) + +taxonomy <- data.frame(taxonomy=tax$taxonomy) +row.names(taxonomy)<- tax$X1 + +# Split taxonomy into separate columns +taxonomy <- data.frame(separate(taxonomy, col=taxonomy, into= c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species"), sep = ";")) + +# Change spaces to NA for missing data +taxonomy <- apply(taxonomy, 2, function(x) gsub("^$|^ $", NA, x)) + +taxonomy <- as.matrix(taxonomy) +taxonomy <- tax_table(taxonomy) + +names(map)[1] <- 'Sample' +row.names(map) <- map$Sample +map[7:9] <- lapply(map[7:9] , factor) + +meta <- sample_data(map) + +input_tree <- read_tree('Data_Files/pruned_tree.tre') + +ps <- phyloseq(otu, taxonomy, meta, input_tree) + +``` + +### Define tree function + +This is the plot tree function from phyloseq. However, I made a few small changes to make it play nicely with plotly. You will not need to change this for your data. Run this step, but you don't have to read all this code. I did so you don't have to, just like Paul McMurdie and Susan Holmes originally wrote the code so I didn't have to. + +```{r define_func} + +interactive_plot_tree <- function (physeq, method = "sampledodge", nodelabf = NULL, color = NULL, + shape = NULL, size = NULL, min.abundance = Inf, label.tips = NULL, + text.size = NULL, sizebase = 5, base.spacing = 0.02, ladderize = FALSE, + plot.margin = 0.2, title = NULL, treetheme = NULL, justify = "jagged", tooltip = NULL) +{ + fix_reserved_vars = function(aesvar) { + aesvar <- gsub("^abundance[s]{0,}$", "Abundance", aesvar, + ignore.case = TRUE) + aesvar <- gsub("^OTU[s]{0,}$", "OTU", aesvar, ignore.case = TRUE) + aesvar <- gsub("^taxa_name[s]{0,}$", "OTU", aesvar, ignore.case = TRUE) + aesvar <- gsub("^sample[s]{0,}$", "Sample", aesvar, ignore.case = TRUE) + return(aesvar) + } + if (!is.null(label.tips)) { + label.tips <- fix_reserved_vars(label.tips) + } + if (!is.null(color)) { + color <- fix_reserved_vars(color) + } + if (!is.null(shape)) { + shape <- fix_reserved_vars(shape) + } + if (!is.null(size)) { + size <- fix_reserved_vars(size) + } + if (is.null(phy_tree(physeq, FALSE))) { + stop("There is no phylogenetic tree in the object you have provided.\n", + "Try phy_tree(physeq) to see for yourself.") + } + if (!inherits(physeq, "phyloseq")) { + method <- "treeonly" + } + treeSegs <- tree_layout(phy_tree(physeq), ladderize = ladderize) + edgeMap = aes(x = xleft, xend = xright, y = y, yend = y) + vertMap = aes(x = x, xend = x, y = vmin, yend = vmax) + p = ggplot(data = treeSegs$edgeDT) + geom_segment(edgeMap) + + geom_segment(vertMap, data = treeSegs$vertDT) + if (is.null(text.size)) { + text.size <- phyloseq:::manytextsize(ntaxa(physeq)) + } + if (!is.null(label.tips) & method != "sampledodge") { + labelDT = treeSegs$edgeDT[!is.na(OTU), ] + if (!is.null(tax_table(object = physeq, errorIfNULL = FALSE))) { + taxDT = data.table(tax_table(physeq), OTU = taxa_names(physeq), + key = "OTU") + labelDT = merge(x = labelDT, y = taxDT, by = "OTU") + } + if (justify == "jagged") { + labelMap <- aes_string(x = "xright", y = "y", label = label.tips, + color = color) + } + else { + labelMap <- aes_string(x = "max(xright, na.rm=TRUE)", + y = "y", label = label.tips, color = color) + } + p <- p + geom_text(labelMap, data = labelDT, size = I(text.size), + hjust = -0.1, na.rm = TRUE) + } + if (is.null(nodelabf)) { + nodelabf = phyloseq:::howtolabnodes(physeq) + } + p = nodelabf(p, treeSegs$edgeDT[!is.na(label), ]) + p = nodelabf(p, treeSegs$vertDT[!is.na(label), ]) + if (is.null(treetheme)) { + treetheme <- theme(axis.ticks = element_blank(), axis.title.x = element_blank(), + axis.text.x = element_blank(), axis.title.y = element_blank(), + axis.text.y = element_blank(), panel.background = element_blank(), + panel.grid.minor = element_blank(), panel.grid.major = element_blank()) + } + if (inherits(treetheme, "theme")) { + p <- p + treetheme + } + if (!is.null(title)) { + p <- p + ggtitle(title) + } + if (method != "sampledodge") { + return(p) + } + dodgeDT = treeSegs$edgeDT[!is.na(OTU), ] + dodgeDT = merge(x = dodgeDT, y = data.table(psmelt(physeq), + key = "OTU"), by = "OTU") + if (justify == "jagged") { + dodgeDT <- dodgeDT[Abundance > 0, ] + } + if (!is.null(color) | !is.null(shape) | !is.null(size)) { + setkeyv(dodgeDT, cols = c("OTU", color, shape, size)) + } + else { + setkey(dodgeDT, OTU, Sample) + } + dodgeDT[, `:=`(h.adj.index, 1:length(xright)), by = OTU] + if (justify == "jagged") { + dodgeDT[, `:=`(xdodge, (xright + h.adj.index * base.spacing * + max(xright, na.rm = TRUE)))] + } + else { + dodgeDT[, `:=`(xdodge, max(xright, na.rm = TRUE) + h.adj.index * + base.spacing * max(xright, na.rm = TRUE))] + dodgeDT <- dodgeDT[Abundance > 0, ] + } + dodgeMap <- aes_string(x = "xdodge", y = "y", color = color, + fill = color, shape = shape, size = size, names=tooltip) + p <- p + geom_point(dodgeMap, data = dodgeDT, na.rm = TRUE) + if (!is.null(size)) { + p <- p + scale_size_continuous(trans = log_trans(sizebase)) + } + if (any(dodgeDT$Abundance >= min.abundance[1])) { + pointlabdf = dodgeDT[Abundance >= min.abundance[1], ] + p <- p + geom_text(mapping = aes(xdodge, y, label = Abundance), + data = pointlabdf, size = text.size, na.rm = TRUE) + } + if (!is.null(label.tips)) { + tiplabDT = dodgeDT + tiplabDT[, `:=`(xfartiplab, max(xdodge)), by = OTU] + tiplabDT <- tiplabDT[h.adj.index == 1, .SD, by = OTU] + if (!is.null(color)) { + if (color %in% sample_variables(physeq, errorIfNULL = FALSE)) { + color <- NULL + } + } + labelMap <- NULL + if (justify == "jagged") { + labelMap <- aes_string(x = "xfartiplab", y = "y", + label = label.tips, color = color) + } + else { + labelMap <- aes_string(x = "max(xfartiplab, na.rm=TRUE)", + y = "y", label = label.tips, color = color) + } + p <- p + geom_text(labelMap, tiplabDT, size = I(text.size), + hjust = -0.1, na.rm = TRUE) + } + min.x <- -0.01 + max.x <- dodgeDT[, max(xright, na.rm = TRUE)] + if ("xdodge" %in% names(dodgeDT)) { + max.x <- dodgeDT[, max(xright, xdodge, na.rm = TRUE)] + } + if (plot.margin > 0) { + max.x <- max.x * (1 + plot.margin) + } + p <- p + scale_x_continuous(limits = c(min.x, max.x)) + + return(p) +} +``` + +### Subset data + +I reduce the data just so plotting is faster. Subset or not as you want to explore your data. subset_samples also is helpful. + +```{r subset} + +ps_ricket <- subset_taxa(ps, Order=='o__Rickettsiales') + + +``` + +### Make reporting taxa + +Here I change the taxa table so that there is a column called lowest_taxa. This is the taxonomic classification of the leaf with the lowest known name. NA's are removed, as are entries such as f__. + +```{r report} + +# Collapse into single column +Lowest_taxa <- do.call(paste, c(as.data.frame(tax_table(ps_ricket))[1:7], sep=";")) + +Lowest_taxa <- gsub("(;NA)*$", "", Lowest_taxa, perl=T) # Remove NAs +Lowest_taxa <- gsub("(;.__)*$", "", Lowest_taxa, perl=T) # Remove g__ +Lowest_taxa <- gsub("^.*;", "", Lowest_taxa, perl=T) # Remove everything to the left of the remaining entry + +# Add back to tax_table +colnames(tax_table(ps_ricket))[1] <- 'Lowest_taxa' +tax_table(ps_ricket)[,'Lowest_taxa'] <- Lowest_taxa + +``` + +This is not necessary, you can use "Family", or whatever taxonomic level you like, by specifying that everywhere you see "Lowest_taxa" below. + + +### Draw Tree and make interactive + +```{r draw_tree} + +pt <- interactive_plot_tree(ps_ricket, ladderize="left", color="Vertposition", size='abundance', nodelabf=nodeplotblank, tooltip = 'Lowest_taxa') + scale_color_manual(values=c("black","saddlebrown","tan2","green1","green4")) + +plotly_tree<-ggplotly(p=pt, tooltip = 'Lowest_taxa') + +plotly_tree + +``` + +This draws the tree. The dots are the different samples we have, colored by sample type. The size of the dot is a measure of abundance. Unfortunately, plotly overwrites color choices, and removes the abundance scale. I will update if I figure out how to fix this. + +You can save as an html using the below. + + +```{r save, eval=F} +htmlwidgets::saveWidget(plotly_tree, 'tree_with_hover.html') +``` + + + diff --git a/tree.png b/tree.png new file mode 100644 index 0000000..4414d7c Binary files /dev/null and b/tree.png differ