Here we are refining bar chart summaries of the taxonomy tables. This is an usual situation in that the dominant taxon makes up over 99% of the total data set. Consequently we will use an “exploding bar chart”. Here we will generate two bar charts for each data set:

  1. Arhodomonas + other
  2. Only the satellite community

Initially this will be conducted at Order level of taxonomy.

Two sets will be generated based on:

  1. 16S amplicon library results
  2. Placement of 16S rRNA metagenomic gene fragments

QIIME2 16S amplicon-library results

MG-RAST 16S rRNA gene fragment top hits

Generating plots based on QIIME2 Silva SSU

QIIME2 processing of amplicon library

Primers used were the “updated” Earth Microbiome Project primers (described here: http://press.igsb.anl.gov/earthmicrobiome/protocols-and-standards/16s/):

  • 515F GTGYCAGCMGCCGCGGTAA
  • 806R GGACTACHVGGGTWTCTAAT

Primers were removed after amplification by Dan Williams, operating the MiSeq sequencer.

for analysis, reads were:

  1. imported into QIIME2 2018.4
  2. denoised, converted to Actual Sequence Variants (ASVs) by dada2 plugin, operated without any truncation
  3. reads were classfied by the QIIME2 sklearn classifier provided with a recomputed Silva v132 515/806 classification model
  4. resulting feature table, taxonomy table, and ASV sequence list were exported and visualized as described below

Importing and parsing the QIIME2 OTU table

OTU.amplib.table <- read.csv("rich.OTU.table.BTEX.csv", header = TRUE)
taxonomy <- data.frame(do.call('rbind', strsplit(as.character(OTU.amplib.table$taxonomy),';',fixed=TRUE)))
colnames(taxonomy) <- c("kingdom","phylum","class","order","family","genus","species")
OTU.amplib.table <- cbind(taxonomy, OTU.amplib.table[-1])
OTU.amplib.table <- data.frame(lapply(OTU.amplib.table, gsub, pattern = "D_\\d__", replacement = ""))
OTU.amplib.table$relative.abund. <- as.numeric(as.character(OTU.amplib.table$relative.abund.))
kable(head(OTU.amplib.table))
kingdom phylum class order family genus species seq.count relative.abund. taxonomy.confidence sequence
Bacteria Proteobacteria Gammaproteobacteria Nitrococcales Nitrococcaceae Arhodomonas uncultured bacterium 453177 0.9931036 0.9567242262 TACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGCGGTCGGATAAGTCGGGTGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTCGATACTGTCTGGCTAGAGTCTGGCAGAGGGAGGTGGAATTTCCGGTGTAGCGGTGAAATGCGTAGATATCGGAAGGAACACCAGTGGCGAAGGCGACCTCCTGGGCCAAGACTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACAGG
Bacteria Firmicutes Clostridia Clostridiales Clostridiaceae 1 Clostridium sensu stricto 13 uncultured bacterium 1276 0.0027963 0.8319686708 TACGTAGGTGGCAAGCGTTGTCCGGATTTACTGGGCGTAAAGGATGCGTAGGCGGACATTTAAGTCAGATGTGAAATACCCGAGCTTAACTTGGGTGCTGCATTTGAAACTGGGTGTCTAGAGTGCAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGACTTACTGGACTGTAACTGACGCTGAGGCATGAAAGCGTGGGGAGCAAACAGG
Bacteria Firmicutes Bacilli 591 0.0012951 0.9850161649 TACGTAGGGGGCAAGCGTTGTCCGGAATGATTGGGCGTAAAGGGCGCGCAGGCGGTTTCGTGTGTCTGATGTGAAAGGCTCCGGCTCAACCGGAGAAGGTCATCGGAAACTGCGGGACTTGAGTCCAGGAGAGGGGAGTGGAATTCCACGTGTAGCGGTGAAATGCGTAGAGATGTGGAGGAACACCAGTGGCGAAGGCGGCTCTCTGGCCTGAGACTGACGCTGAGGCGCGAAAGCGTGGGGAGCGAACAGG
Bacteria Firmicutes Clostridia Clostridiales Peptococcaceae Candidatus Dichloromethanomonas uncultured bacterium 201 0.0004405 0.9184785956 TACGTAGGGGGCAAGCGTTGTCCGGAATCATTGGGCGTAAAGGGCGCGTAGGCGGGCATATAAGTCTGATGTGAAAGTGCGGTGCTTAACACCGTAAAGCATTGGAAACTGTATGTCTTGAGGACAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGACTGTAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCGAACAGG
Bacteria Firmicutes Bacilli Bacillales Bacillaceae Bacillus uncultured compost bacterium 191 0.0004186 0.7080361989 TACGTAGGGGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCGCGCAGGCGGCTTCTTAAGTCTGATGTGAAAGCCCGTGGCTCAACCACGGAGGGGCATTGGAAACTGGGGAGCTTGAGTACAGGAGAGGAGAGCGGAATTCCACGTGTAGCGGTGAAATGCGTAGAGATGTGGAGGAACACCAGTGGCGAAAGCGGCTCTCTGGCCTGTAACTGACGCTGAGGCGCGAAAGCGTGGGGAGCGAACAGG
Bacteria Proteobacteria Deltaproteobacteria Desulfovibrionales Desulfovibrionaceae Desulfovibrio Desulfovibrio desulfuricans 101 0.0002213 0.8360530132 TACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGTAGGCTGTAGTGTAAGTCAGGGGTGAAATCCCACGGCTCAACCGTGGAACTGCCTTTGATACTGCACAACTTGAATCCGGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACATCAGTGGCGAAGGCGGCCACCTGGACCGGTATTGACGCTGAGGTGCGAAAGCGTGGGGAGCAAACAGG

Plotting based on Class

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
OTU.amplib.class <- dplyr::group_by(OTU.amplib.table, class)
OTU.amplib.class.summary <- dplyr::summarise(OTU.amplib.class, class.total = sum(relative.abund.))
OTU.amplib.class.summary <- OTU.amplib.class.summary[order(-OTU.amplib.class.summary$class.total),]
OTU.amplib.class.summary$class[OTU.amplib.class.summary$class == ""] <- "NA"
## Warning in `[<-.factor`(`*tmp*`, OTU.amplib.class.summary$class == "",
## value = structure(c(10L, : invalid factor level, NA generated
kable(OTU.amplib.class.summary)
class class.total
Gammaproteobacteria 0.9937457
Clostridia 0.0033463
Bacilli 0.0018781
Deltaproteobacteria 0.0002805
Bacteroidia 0.0001578
Cloacimonadia 0.0001205
Spirochaetia 0.0000986
Thermotogae 0.0000986
Rhodothermia 0.0000416
Anaerolineae 0.0000394
LD1-PA32 0.0000373
Methanobacteria 0.0000351
Synergistia 0.0000329
Methanomicrobia 0.0000219
NA 0.0000153
Hydrogenedentia 0.0000153
Caldatribacteriia 0.0000131
Thermoleophilia 0.0000088
Actinobacteria 0.0000066
Verrucomicrobiae 0.0000066 
library(ggplot2)
library(ggthemes)
OTU.amplib.class.major <- rbind(data.frame(OTU.amplib.class.summary[1,]),data.frame(OTU.amplib.class.summary[1,]))
OTU.amplib.class.major$class.total <- as.numeric(as.character(OTU.amplib.class.major$class.total))
OTU.amplib.class.major$class <- as.character(OTU.amplib.class.major$class)
OTU.amplib.class.major[1,1] <- "other" 
OTU.amplib.class.major[1,2] <- 1 - OTU.amplib.class.major[1,2]
OTU.amplib.class.major$class <- factor(OTU.amplib.class.major$class, levels = OTU.amplib.class.major$class, ordered = T)

colors <- c(
  "#a0a0a0","#e59090", "#5F7FC7", "orange","#DA5724", "#508578", "#CD9BCD",
  "#AD6F3B", "#673770","#D14285", "#652926", "#C84248", 
  "#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861")

ggplot(OTU.amplib.class.major, aes(fill=class, x="x", y=class.total)) +
    geom_bar( stat="identity", position="fill") + scale_fill_manual(values = colors) +
    theme_void()

amplib.major <- ggplot(OTU.amplib.class.major, aes(fill=class, x="x", y=class.total)) +
    geom_bar( stat="identity", position="fill") + scale_fill_manual(values = colors) +
    theme_void()

ggsave("amplib.major.png", amplib.major, height = 10, width = 4)

Plotting minor community members, grouping everything with representation of less than 0.0001 as “other”

library(ggplot2)
library(rlist)
OTU.amplib.class.summary.sat <- OTU.amplib.class.summary[-1,]
OTU.amplib.class.summary.sat$class <- as.character(OTU.amplib.class.summary.sat$class)
OTU.amplib.class.summary.sat.top <- OTU.amplib.class.summary.sat[c(1:6),]
OTU.amplib.class.summary.sat.bot <- OTU.amplib.class.summary.sat[-c(1:6),]
OTU.amplib.class.summary.sat.top[6,1] <- "other"
OTU.amplib.class.summary.sat.top[6,2] <- sum(OTU.amplib.class.summary.sat.bot$class.total)
#OTU.amplib.class.summary.sat <- OTU.amplib.class.summary.sat[order(OTU.amplib.class.summary.sat$class.total),]
OTU.amplib.class.summary.sat.top$class <- factor(OTU.amplib.class.summary.sat.top$class, levels = list.reverse(OTU.amplib.class.summary.sat.top$class), ordered = T)

colors2 <- c(
  "#a0a0a0","#e59090", "#5F7FC7", "orange","#DA5724", "#508578", "#CD9BCD",
  "#AD6F3B", "#673770","#D14285", "#652926", "#C84248", 
  "#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861","#4286f4")

amplib.minor <- ggplot(OTU.amplib.class.summary.sat.top, aes(fill=class, x="x", y=class.total)) + scale_fill_manual(values = colors2) +
    geom_bar( stat="identity", position="fill") +
    theme_void()

amplib.minor

ggsave("amplib.minor.png", amplib.minor, height = 10, width = 4)

Generating plots based on MG-RAST Silva SSU placements

Import and parse the table

SILVASSU30 <- read.csv("MG-RAST/final.SilvaSSU.e30.tsv", header=TRUE, sep="\t")
SILVASSU30 <- SILVASSU30[-8]
SILVASSU30 <- SILVASSU30[order(-SILVASSU30$BTEX1.unmapped.FR.fastq),]
SILVASSU30$BTEX1.unmapped.FR.fastq <- SILVASSU30$BTEX1.unmapped.FR.fastq / sum(SILVASSU30$BTEX1.unmapped.FR.fastq)
SILVASSU30$Kuwait.1810.mgenome.fastq <- SILVASSU30$Kuwait.1810.mgenome.fastq / sum(SILVASSU30$Kuwait.1810.mgenome.fastq)
colnames(SILVASSU30) <- c("domain","phylum","class","order","family","genus","unmapped","full.mgenome")
kable(SILVASSU30)
domain phylum class order family genus unmapped full.mgenome
3 Bacteria Proteobacteria Gammaproteobacteria Chromatiales Ectothiorhodospiraceae Arhodomonas 0.9170452 0.9009120
23 unclassified sequences unclassified (derived from unclassified sequences) unclassified (derived from unclassified sequences) unclassified (derived from unclassified sequences) unclassified (derived from unclassified sequences) unclassified (derived from unclassified sequences) 0.0597080 0.0589529
21 Bacteria Proteobacteria Gammaproteobacteria unclassified (derived from Gammaproteobacteria) unclassified (derived from Gammaproteobacteria) unclassified (derived from Gammaproteobacteria) 0.0117813 0.0179613
20 Bacteria unclassified (derived from Bacteria) unclassified (derived from Bacteria) unclassified (derived from Bacteria) unclassified (derived from Bacteria) unclassified (derived from Bacteria) 0.0099109 0.0194889
4 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Bacillus 0.0006802 0.0004398
11 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Geobacillus 0.0002915 0.0003009
12 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Marinococcus 0.0001700 0.0002315
2 Bacteria Firmicutes Bacilli Bacillales Alicyclobacillaceae Alicyclobacillus 0.0001457 0.0001157
7 Bacteria Firmicutes Clostridia Clostridiales Peptococcaceae Desulfotomaculum 0.0000972 0.0000000
9 Bacteria Firmicutes Bacilli Lactobacillales Enterococcaceae Enterococcus 0.0000729 0.0000000
6 Bacteria Firmicutes Clostridia Clostridiales Peptococcaceae Desulfosporosinus 0.0000486 0.0000000
19 Bacteria Firmicutes Bacilli Bacillales Bacillaceae Virgibacillus 0.0000486 0.0000694
1 Bacteria Firmicutes Clostridia Clostridiales Eubacteriaceae Acetobacterium 0.0000000 0.0000694
5 Eukaryota Nematoda Chromadorea Rhabditida Rhabditidae Caenorhabditis 0.0000000 0.0000463
8 Bacteria Proteobacteria Gammaproteobacteria Chromatiales Ectothiorhodospiraceae Ectothiorhodospira 0.0000000 0.0000926
10 Bacteria Firmicutes Clostridia Clostridiales Eubacteriaceae Eubacterium 0.0000000 0.0000694
13 Bacteria Proteobacteria Gammaproteobacteria unclassified (derived from Gammaproteobacteria) unclassified (derived from Gammaproteobacteria) Methylonatrum 0.0000000 0.0000463
14 Bacteria Proteobacteria Gammaproteobacteria Chromatiales Ectothiorhodospiraceae Nitrococcus 0.0000000 0.0006018
15 Bacteria Firmicutes Bacilli Bacillales Paenibacillaceae Paenibacillus 0.0000000 0.0001157
16 Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Moraxellaceae Psychrobacter 0.0000000 0.0001157
17 Bacteria Firmicutes Bacilli Bacillales Sporolactobacillaceae Sporolactobacillus 0.0000000 0.0000694
18 Bacteria Proteobacteria Gammaproteobacteria Chromatiales Ectothiorhodospiraceae Thioalkalivibrio 0.0000000 0.0002315
22 Bacteria Proteobacteria unclassified (derived from Proteobacteria) unclassified (derived from Proteobacteria) unclassified (derived from Proteobacteria) unclassified (derived from Proteobacteria) 0.0000000 0.0000694

Summarize by Class

library(dplyr)
OTU.mgrast.class <- dplyr::group_by(SILVASSU30, class)
OTU.mgrast.class.summary <- dplyr::summarise(OTU.mgrast.class, class.total = sum(unmapped))
OTU.mgrast.class.summary <- OTU.mgrast.class.summary[order(-OTU.mgrast.class.summary$class.total),]
OTU.mgrast.class.summary$class[OTU.mgrast.class.summary$class == ""] <- "NA"
## Warning in `[<-.factor`(`*tmp*`, OTU.mgrast.class.summary$class == "",
## value = structure(c(4L, : invalid factor level, NA generated
OTU.mgrast.class.summary$class <- as.character(OTU.mgrast.class.summary$class)
OTU.mgrast.class.summary <- OTU.mgrast.class.summary[-c(2,3,7),] # removing unclassified fragments
OTU.mgrast.class.summary$class.total <- OTU.mgrast.class.summary$class.total/sum(OTU.mgrast.class.summary$class.total)
kable(OTU.mgrast.class.summary)
class class.total
Gammaproteobacteria 0.9983290
Bacilli 0.0015143
Clostridia 0.0001567
Chromadorea 0.0000000

Plotting Arhodomonas and unclassified seqs

library(ggplot2)
library(ggthemes)
OTU.mgrast.class.major <- rbind(data.frame(OTU.mgrast.class.summary[1,]),data.frame(OTU.mgrast.class.summary[1,]))
OTU.mgrast.class.major$class.total <- as.numeric(as.character(OTU.mgrast.class.major$class.total))
OTU.mgrast.class.major$class <- as.character(OTU.mgrast.class.major$class)
OTU.mgrast.class.major[2,1] <- "other" 
OTU.mgrast.class.major[2,2] <- 1 - OTU.mgrast.class.major[1,2]
OTU.mgrast.class.major <- OTU.mgrast.class.major[order(OTU.mgrast.class.major$class.total),]
OTU.mgrast.class.major$class <- factor(OTU.mgrast.class.major$class, levels = OTU.mgrast.class.major$class, ordered = T)

colors <- c(
  "#a0a0a0","#e59090", "#5F7FC7", "orange","#DA5724", "#508578", "#CD9BCD",
  "#AD6F3B", "#673770","#D14285", "#652926", "#C84248", 
  "#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861")

ggplot(OTU.mgrast.class.major, aes(fill=class, x="x", y=class.total)) +
    geom_bar( stat="identity", position="fill") + scale_fill_manual(values = colors) +
    theme_void()

mgrast.major <- ggplot(OTU.mgrast.class.major, aes(fill=class, x="x", y=class.total)) +
    geom_bar( stat="identity", position="fill") + scale_fill_manual(values = colors) +
    theme_void()

ggsave("mgrast.major.png", amplib.major, height = 10, width = 4)

Plotting the satellite community

library(ggplot2)
library(rlist)
OTU.mgrast.class.summary.sat <- OTU.mgrast.class.summary[-c(1,4),]
OTU.mgrast.class.summary.sat$class <- as.character(OTU.mgrast.class.summary.sat$class)
#OTU.mgrast.class.summary.sat.top <- OTU.mgrast.class.summary.sat[c(1:6),]
#OTU.mgrast.class.summary.sat.bot <- OTU.mgrast.class.summary.sat[-c(1:6),]
#OTU.mgrast.class.summary.sat.top[6,1] <- "other"
#OTU.mgrast.class.summary.sat.top[6,2] <- sum(OTU.mgrast.class.summary.sat.bot$class.total)
#OTU.mgrast.class.summary.sat <- OTU.mgrast.class.summary.sat[order(OTU.mgrast.class.summary.sat$class.total),]
OTU.mgrast.class.summary.sat$class <- factor(OTU.mgrast.class.summary.sat$class, levels = list.reverse(OTU.mgrast.class.summary.sat$class), ordered = T)

colors2 <- c(
  "#DA5724", "#508578", "#CD9BCD",
  "#AD6F3B", "#673770","#D14285", "#652926", "#C84248", 
  "#8569D5", "#5E738F","#D1A33D", "#8A7C64", "#599861","#4286f4")

mgrast.minor <- ggplot(OTU.mgrast.class.summary.sat, aes(fill=class, x="x", y=class.total)) + scale_fill_manual(values = colors2) +
    geom_bar( stat="identity", position="fill") +
    theme_void()

mgrast.minor

ggsave("mgrast.minor.png", mgrast.minor, height = 10, width = 4)

These figures are assembled into exploding-bar-charts in PowerPoint. Figures shown at the beginning of the document.