Chapter 11 Visualization
XMAS 2.0 provides mulitple functions for visualization. For instance, using plot_volcano to display the results of differential analysis.
Outline of this Chapter:
11.2 Importing Data
data("amplicon_ps")
amplicon_ps_rarefy <- norm_rarefy(object = amplicon_ps,
size = 1114)
amplicon_ps_rarefy## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 2308 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 8 sample variables ]
## tax_table() Taxonomy Table: [ 2308 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 2308 tips and 2306 internal nodes ]11.3 plot_boxplot
- calculate alpha diversity
dat_alpha <- run_alpha_diversity(ps = amplicon_ps_rarefy,
measures = c("Shannon", "Chao1", "Observed"))
head(dat_alpha)## TempRowNames SampleType Year Month Day Subject ReportedAntibioticUsage DaysSinceExperimentStart Description Observed Chao1
## 1 L1S140 gut 2008 10 28 2 Yes 0 2_Fece_10_28_2008 27 74.50
## 2 L1S208 gut 2009 1 20 2 No 84 2_Fece_1_20_2009 40 148.75
## 3 L1S8 gut 2008 10 28 1 Yes 0 1_Fece_10_28_2008 19 54.00
## 4 L1S281 gut 2009 4 14 2 No 168 2_Fece_4_14_2009 60 256.00
## 5 L3S242 right palm 2008 10 28 1 Yes 0 1_R_Palm_10_28_2008 16 42.00
## 6 L2S309 left palm 2009 1 20 2 No 84 2_L_Palm_1_20_2009 12 57.00
## se.chao1 Shannon
## 1 30.70970 3.126005
## 2 62.86107 3.303186
## 3 25.57190 2.688337
## 4 93.42352 3.664947
## 5 19.97805 2.692311
## 6 30.06061 2.082828plot_boxplot has many parameters, and help you enjoy it.
- single measure
Figure 11.1: boxplot(single measure)
- single measure with significant results
Figure 11.2: boxplot(single measure with significant results)
- single measure with significant results of pairwises
plot_boxplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
cmp_list = list(c("gut", "right palm"),
c("gut", "tongue")))
Figure 11.3: boxplot(single measure with significant results of pairwises)
- single measure with significant results of pairwises and outlier
plot_boxplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
cmp_list = list(c("gut", "right palm"),
c("gut", "tongue")),
outlier = TRUE)
Figure 11.4: boxplot(single measure with significant results of pairwises and outlier)
- single measure with significant results of ref_group
plot_boxplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
ref_group = "gut")
Figure 11.5: boxplot(single measure with significant results of ref_group)
- multiple measures
plot_boxplot(data = dat_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "SampleType",
group_names = c("gut", "right palm", "tongue"),
group_color = c("red", "green", "blue"),
ref_group = "gut",
method = "wilcox.test",
outlier = TRUE)
Figure 11.6: boxplot(multiple measure with group number)
- show group_number in the x-axis break
plot_boxplot(data = dat_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "SampleType",
group_names = c("gut", "right palm", "tongue"),
group_color = c("red", "green", "blue"),
do_test = TRUE,
method = "wilcox.test",
group_number = TRUE)
Figure 11.7: boxplot(group_number)
11.4 plot_barplot
plot_barplot has many parameters, and help you enjoy it.
- single measure
Figure 11.8: barplot(single measure)
- single measure with significant results
Figure 11.9: barplot(single measure with significant results)
- single measure with significant results of pairwises
plot_barplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
cmp_list = list(c("gut", "right palm"), c("gut", "tongue")))
Figure 11.10: barplot(single measure with significant results of pairwises)
- single measure with significant results of ref_group
plot_barplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
ref_group = "gut")
Figure 11.11: barplot(single measure with significant results of ref_group)
- multiple index
plot_barplot(data = dat_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "SampleType",
do_test = TRUE,
method = "wilcox.test")
Figure 11.12: barplot(multiple index)
- show group_number in the x-axis break
plot_barplot(data = dat_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "SampleType",
group_names = c("gut", "right palm", "tongue"),
group_color = c("red", "green", "blue"),
do_test = TRUE,
method = "wilcox.test",
group_number = TRUE)
Figure 11.13: barplot(group_number)
11.5 plot_dotplot
plot_dotplot has many parameters, and help you enjoy it.
- single measure
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.14: dotplot(single measure)
- single measure with significant results
## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.15: dotplot(single measure with significant results)
- single measure with significant results of pairwises
plot_dotplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
cmp_list = list(c("gut", "right palm"),
c("gut", "tongue")))## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.16: dotplot(single measure with significant results of pairwises)
- single measure with significant results of ref_group
plot_dotplot(
data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
ref_group = "gut")## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.17: dotplot(single measure with significant results of ref_group)
- dot size and median size
plot_dotplot(
data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
ref_group = "gut",
dotsize = 0.5,
mediansize = 2)## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.18: dotplot(dot size and median size)
- multiple index
plot_dotplot(
data = dat_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "SampleType",
do_test = TRUE,
method = "wilcox.test")## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.19: dotplot(multiple index)
- multiple index with errorbar
plot_dotplot(
data = dat_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "SampleType",
do_test = TRUE,
show_type = "errorbar",
method = "wilcox.test")## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.20: dotplot(multiple index errorbar)
- show group_number in the x-axis break
plot_dotplot(data = dat_alpha,
y_index = c("Shannon", "Chao1", "Observed"),
group = "SampleType",
group_names = c("gut", "right palm", "tongue"),
group_color = c("red", "green", "blue"),
show_type = "errorbar",
do_test = TRUE,
method = "wilcox.test",
group_number = TRUE)## Bin width defaults to 1/30 of the range of the data. Pick better value with `binwidth`.
Figure 11.21: dotplot(group_number)
11.6 plot_correlation_boxplot
Help you enjoy plot_correlation_boxplot.
plot_correlation_boxplot(
data = dat_alpha,
x_index = "Chao1",
y_index = "Shannon",
group = "SampleType")
Figure 11.22: correlation with boxplot
11.7 plot_correlation_density
Help you enjoy plot_correlation_density.
plot_correlation_density(
data = dat_alpha,
x_index = "Chao1",
y_index = "Shannon",
group = "SampleType")
Figure 11.23: correlation with density
11.8 plot_Ordination
plot_Ordination provides too many parameters for users to display the ordination results by using ggplot2 format. Here is the ordinary pattern.
data("dada2_ps")
# step1: Removing samples of specific group in phyloseq-class object
dada2_ps_remove_BRS <- get_GroupPhyloseq(
ps = dada2_ps,
group = "Group",
group_names = "QC")
# step2: Rarefying counts in phyloseq-class object
dada2_ps_rarefy <- norm_rarefy(object = dada2_ps_remove_BRS,
size = 51181)
# step3: Extracting specific taxa phyloseq-class object
dada2_ps_rare_genus <- summarize_taxa(ps = dada2_ps_rarefy,
taxa_level = "Genus",
absolute = TRUE)
# step4: Aggregating low relative abundance or unclassified taxa into others
# dada2_ps_genus_LRA <- summarize_LowAbundance_taxa(ps = dada2_ps_rare_genus,
# cutoff = 10,
# unclass = TRUE)
# step4: Filtering the low relative abundance or unclassified taxa by the threshold
dada2_ps_genus_filter <- run_filter(ps = dada2_ps_rare_genus,
cutoff = 10,
unclass = TRUE)
# step5: Trimming the taxa with low occurrence less than threshold
dada2_ps_genus_filter_trim <- run_trim(object = dada2_ps_genus_filter,
cutoff = 0.2,
trim = "feature")
dada2_ps_genus_filter_trim## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 83 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 83 taxa by 6 taxonomic ranks ]ordination_PCA <- run_ordination(
ps = dada2_ps_genus_filter_trim,
group = "Group",
method = "PCA")
names(ordination_PCA)## [1] "fit" "dat" "explains" "eigvalue" "PERMANOVA" "BETADISPER" "axis_taxa_cor"- Ordinary pattern
Figure 11.24: plot_Ordination (Ordinary pattern)
- plot with SampleID and setting group colors
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse",
sample = TRUE)
Figure 11.25: plot_Ordination (Ordinary pattern with SampleID)
- ellipse with 95% confidence interval
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse_CI",
sample = TRUE)
Figure 11.26: plot_Ordination (ellipse with 95% confidence interval)
- ellipse with groups
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse_groups",
sample = TRUE)
Figure 11.27: plot_Ordination (ellipse with groups)
- ellipse with border line
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse_line",
sample = TRUE)
Figure 11.28: plot_Ordination (ellipse with border line)
- plot with SampleID and sideboxplot and setting group colors
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse",
sidelinechart = FALSE,
sideboxplot = TRUE,
sample = TRUE)
Figure 11.29: plot_Ordination (Ordinary pattern with SampleID sideboxplot)
- plot with SampleID and sideboxplot and setting group colors 2
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse_CI",
sidelinechart = FALSE,
sideboxplot = TRUE,
sample = TRUE)
Figure 11.30: plot_Ordination (ellipse_CI with SampleID sideboxplot)
- plot with SampleID and sideboxplot and setting group colors 3
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse_groups",
sidelinechart = FALSE,
sideboxplot = TRUE,
sample = TRUE)
Figure 11.31: plot_Ordination (ellipse_groups with SampleID sideboxplot)
- plot with SampleID and sideboxplot and setting group colors 4
plot_Ordination(ResultList = ordination_PCA,
group = "Group",
group_names = c("AA", "BB"),
group_color = c("blue", "red"),
circle_type = "ellipse_line",
sidelinechart = FALSE,
sideboxplot = TRUE,
sample = TRUE)
Figure 11.32: plot_Ordination (ellipse_line with SampleID sideboxplot)
- plot with SampleID and sideboxplot and setting group colors and shape
data("amplicon_ps")
amplicon_ps_genus <- summarize_taxa(ps = amplicon_ps,
taxa_level = "Genus")
amplicon_res_ordination <- run_ordination(
ps = amplicon_ps_genus,
group = "SampleType",
method = "PCoA")## [1] "Pvalue of beta dispersion less than 0.05"plot_Ordination(ResultList = amplicon_res_ordination,
group = "SampleType",
shape_column = "ReportedAntibioticUsage",
shape_values = c(16, 17),
circle_type = "ellipse_line",
sidelinechart = FALSE,
sideboxplot = TRUE,
sample = TRUE)
Figure 11.33: plot_Ordination (ellipse_line with SampleID sideboxplot)
11.9 plot_ggbiplot
- biplot with topN dominant taxa
plot_ggbiplot(ResultList = ordination_PCA,
group = "Group",
group_color = c("blue", "red"),
topN = 5,
ellipse = TRUE,
labels = "SampleID")
Figure 11.34: plot_ggbiplot (biplot)
11.10 plot_corrplot
dada2_beta <- run_beta_diversity(ps = dada2_ps_rarefy,
method = "bray")
plot_distance_corrplot(datMatrix = dada2_beta$BetaDistance)
Figure 11.35: plot_corrplot (distance)
11.11 plot_2DA_venn
da_wilcox <- run_wilcox(
ps = dada2_ps_genus_filter_trim,
group = "Group",
group_names = c("AA", "BB"))
da_ttest <- run_ttest(
ps = dada2_ps_genus_filter_trim,
group = "Group",
group_names = c("AA", "BB"))
DA_venn_res <- plot_2DA_venn(
daTest1 = da_wilcox,
daTest2 = da_ttest,
datType1 = "AA vs BB(wilcox)",
datType2 = "AA vs BB(t-test)",
group_names = c("AA", "BB"),
Pvalue_name = "Pvalue",
logFc_name1 = "Log2FoldChange (Rank)\nAA_vs_BB",
logFc_name2 = "Log2FoldChange (Mean)\nAA_vs_BB",
Pvalue_cutoff = 0.8,
logFC_cutoff = 0.2)
DA_venn_res$pl
Figure 11.36: plot_2DA_venn (wilcox vs t_test)
11.12 plot the DA results from the significant taxa by double barplot
data("amplicon_ps")
DA_res <- run_wilcox(
ps = amplicon_ps,
taxa_level = "Family",
group = "SampleType",
group_names = c("tongue", "gut"))
plot_double_barplot(data = DA_res,
x_index = "Log2FoldChange (Rank)\ntongue_vs_gut",
x_index_cutoff = 1,
y_index = "AdjustedPvalue",
y_index_cutoff = 0.05)
Figure 11.37: double barplot for DA results
11.13 plot_stacked_bar_XIVZ
- Minimum usage: plot in relative abundance
Figure 11.38: plot_stacked_bar_XIVZ (test1)
- Set feature parameter to show feature information
Figure 11.39: plot_stacked_bar_XIVZ (test2)
- Pass ordered sample names to order parameter to plot in specific order
metadata <- phyloseq::sample_data(dada2_ps_rarefy) %>%
data.frame() %>%
dplyr::arrange(Group)
plot_stacked_bar_XIVZ(phyloseq = dada2_ps_rarefy,
level = "Family",
feature = "Group",
order = rownames(metadata))
Figure 11.40: plot_stacked_bar_XIVZ (test3)
- Use facet_wrap(vars(), scale=“free”) funciton to facet stacked barplot
plot_stacked_bar_XIVZ(phyloseq = dada2_ps_rarefy,
level = "Family",
relative_abundance = TRUE,
order = rownames(metadata)) +
facet_wrap(vars(Group), scale="free")
Figure 11.41: plot_stacked_bar_XIVZ (test4)
11.14 plot_StackBarPlot
plot_StackBarPlot provides too many parameters for users to display the Stacked barplot of microbial composition by using ggplot2 format. Here is the ordinary pattern. More details to see help(plot_StackBarPlot).
- Ordinary pattern
Figure 11.42: plot_StackBarPlot(Ordinary pattern)
- Metadata with
SampleTypephenotype
## [1] "This palatte have 20 colors!"
Figure 11.43: plot_StackBarPlot (Metadata with group)
- Metadata with
SampleTypephenotype in cluster mode
plot_StackBarPlot(
ps = amplicon_ps_rarefy,
taxa_level = "Phylum",
group = "SampleType",
cluster = TRUE)## [1] "This palatte have 20 colors!"
Figure 11.44: plot_StackBarPlot (Metadata with group in cluster mode)
- Metadata with
SampleTypephenotype in facet
plot_StackBarPlot(
ps = amplicon_ps_rarefy,
taxa_level = "Phylum",
group = "SampleType",
facet = TRUE)
Figure 11.45: plot_StackBarPlot (Metadata with group in facet)
- Metadata with two groups to display samples
plot_StackBarPlot(
ps = amplicon_ps_rarefy,
taxa_level = "Order",
group = "SampleType",
subgroup = "Year")## [1] "This palatte have 19 colors!"
## [1] "This palatte have 20 colors!"
## [1] "This palatte have 20 colors!"
## [1] "This palatte have 20 colors!"
Figure 11.46: plot_StackBarPlot (Metadata with two groups ot display samples)
- Metadata with three groups to display samples
plot_StackBarPlot(
ps = amplicon_ps_rarefy,
taxa_level = "Order",
group = "SampleType",
subgroup = c("Year", "Month"))## [1] "This palatte have 19 colors!"
## [1] "This palatte have 20 colors!"
## [1] "This palatte have 20 colors!"
## [1] "This palatte have 20 colors!"
## [1] "This palatte have 20 colors!"
## [1] "This palatte have 20 colors!"
Figure 11.47: plot_StackBarPlot (Metadata with three groups ot display samples)
- Order SampleID by orderSample parameter
plot_StackBarPlot(
ps = amplicon_ps_rarefy,
taxa_level = "Order",
group = "SampleType",
orderSample = phyloseq::sample_names(amplicon_ps)[1:10])## [1] "This palatte have 20 colors!"
Figure 11.48: Stacked barplot with Ordered Samples
- Hide sample names by sample_label parameter
plot_StackBarPlot(
ps = amplicon_ps_rarefy,
taxa_level = "Order",
group = "SampleType",
orderSample = phyloseq::sample_names(amplicon_ps)[1:10],
sample_label = FALSE)## [1] "This palatte have 20 colors!"
Figure 11.49: Stacked barplot with hiding Samples’ names
11.15 Color Palettes
11.15.1 Wes Anderson Palettes
Wes Anderson Palettes is from wesanderson package and we have integrated it into XMAS2.0.
data("amplicon_ps")
dat_alpha <- run_alpha_diversity(ps = amplicon_ps, measures = c("Shannon", "Chao1"))
# origin
pl_origin <- plot_boxplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
do_test = TRUE,
cmp_list = list(c("gut", "right palm"), c("gut", "left palm")),
method = "wilcox.test")
# Wes Anderson Palettes
pal <- wes_palette(name = "GrandBudapest1", 4, type = "discrete")
pl_wes <- plot_boxplot(data = dat_alpha,
y_index = "Shannon",
group = "SampleType",
group_color = pal,
do_test = TRUE,
cmp_list = list(c("gut", "right palm"), c("gut", "left palm")),
method = "wilcox.test")
cowplot::plot_grid(pl_origin, pl_wes,
align = "hv",
labels = c("Origin", "Wes Anderson"))
Figure 11.50: Wes Anderson Palettes
11.16 Ordination plots with ggplot2
11.16.1 principal components analysis with the iris data set
ord <- prcomp(iris[, 1:4])
ggord(ord, iris$Species, cols = c('purple', 'orange', 'blue')) +
scale_shape_manual('Groups', values = c(1, 2, 3)) +
theme_classic() +
theme(legend.position = 'top')
Figure 11.52: principal components analysis
11.16.2 multiple correspondence analysis with the tea dataset
data(tea, package = 'FactoMineR')
tea <- tea[, c('Tea', 'sugar', 'price', 'age_Q', 'sex')]
ord <- FactoMineR::MCA(tea[, -1], graph = FALSE)
ggord(ord, tea$Tea, parse = FALSE) # use parse = FALSE for labels with non alphanumeric characters
Figure 11.53: multiple correspondence analysis
11.17 Systematic Information
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.3 (2022-03-10)
## os macOS Monterey 12.2.1
## system x86_64, darwin17.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2023-11-30
## rstudio 2023.09.0+463 Desert Sunflower (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
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## ade4 1.7-22 2023-02-06 [2] CRAN (R 4.1.2)
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## base64enc 0.1-3 2015-07-28 [2] CRAN (R 4.1.0)
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## Biobase 2.54.0 2021-10-26 [2] Bioconductor
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## cli 3.6.1 2023-03-23 [2] CRAN (R 4.1.2)
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## colorspace 2.1-0 2023-01-23 [2] CRAN (R 4.1.2)
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## corrplot 0.92 2021-11-18 [2] CRAN (R 4.1.0)
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##
## [1] /Users/zouhua/Library/R/x86_64/4.1/library
## [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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