miaViz
1 May 2024
Contents
miaViz implements plotting function to work with TreeSummarizedExperiment
and related objects in a context of microbiome analysis. For more general
plotting function on SummarizedExperiment objects the scater package offers
several options, such as plotColData, plotExpression and plotRowData.
1 Installation
To install miaViz, install BiocManager first, if it is not installed.
Afterwards use the install function from BiocManager and load miaViz.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("miaViz")
#> Bioconductor version 3.19 (BiocManager 1.30.22), R 4.4.0 beta (2024-04-15
#> r86425)
#> Warning: package(s) not installed when version(s) same as or greater than current; use
#> `force = TRUE` to re-install: 'miaViz'library(miaViz)
data(GlobalPatterns, package = "mia")2 Abundance plotting
in contrast to other fields of sequencing based fields of research for which
expression of genes is usually studied, microbiome research uses the more
term Abundance to described the numeric data measured and analyzed.
Technically, especially in context of SummarizedExperiment objects, there is
no difference. Therefore plotExpression can be used to plot Abundance data.
plotAbundance can be used as well and as long as rank is set NULL, it
behaves as plotExpression.
plotAbundance(GlobalPatterns, rank = NULL,
features = "549322", assay.type = "counts")
However, if the rank is set not NULL a bar plot is returned. At the same
time the features argument can be set to NULL (default).
GlobalPatterns <- transformAssay(GlobalPatterns, method = "relabundance")plotAbundance(GlobalPatterns, rank = "Kingdom", assay.type = "relabundance")
With subsetting to selected features the plot can be fine tuned.
prev_phylum <- getPrevalentTaxa(GlobalPatterns, rank = "Phylum",
detection = 0.01, onRankOnly = TRUE)
#> Warning in getPrevalentTaxa(GlobalPatterns, rank = "Phylum", detection = 0.01,
#> : The 'getPrevalentTaxa' function is deprecated. Use 'getPrevalentFeatures'
#> instead.plotAbundance(GlobalPatterns[rowData(GlobalPatterns)$Phylum %in% prev_phylum],
rank = "Phylum",
assay.type = "relabundance")
The features argument is reused for plotting data along the different samples.
In the next example the SampleType is plotted along the samples. In this case
the result is a list, which can combined using external tools, for example
patchwork.
library(patchwork)
plots <- plotAbundance(GlobalPatterns[rowData(GlobalPatterns)$Phylum %in% prev_phylum],
features = "SampleType",
rank = "Phylum",
assay.type = "relabundance")
plots$abundance / plots$SampleType +
plot_layout(heights = c(9, 1))
Further example about composition barplot can be found at Orchestrating Microbiome Analysis (Lahti, Shetty, and Ernst 2021).
3 Prevalence plotting
To visualize prevalence within the dataset, two functions are available,
plotFeaturePrevalence, plotPrevalenceAbundance and plotPrevalence.
plotFeaturePrevalence produces a so-called landscape plot, which
visualizes the prevalence of samples across abundance thresholds.
plotFeaturePrevalence(GlobalPatterns, rank = "Phylum",
detections = c(0, 0.001, 0.01, 0.1, 0.2))
plotPrevalenceAbundance plot the prevalence depending on the mean relative
abundance on the chosen taxonomic level.
plotPrevalentAbundance(GlobalPatterns, rank = "Family",
colour_by = "Phylum") +
scale_x_log10()
plotPrevalence plot the number of samples and their prevalence across
different abundance thresholds. Abundance steps can be adjusted using the
detections argument, whereas the analyzed prevalence steps is set using the
prevalences argument.
plotPrevalence(GlobalPatterns,
rank = "Phylum",
detections = c(0.01, 0.1, 1, 2, 5, 10, 20)/100,
prevalences = seq(0.1, 1, 0.1))
4 Tree plotting
The information stored in the rowTree can be directly plotted. However,
sizes of stored trees have to be kept in mind and plotting of large trees
rarely makes sense.
For this example we limit the information plotted to the top 100 taxa as judged by mean abundance on the genus level.
library(scater)
library(mia)altExp(GlobalPatterns,"Genus") <- agglomerateByRank(GlobalPatterns,"Genus")
altExp(GlobalPatterns,"Genus") <- addPerFeatureQC(altExp(GlobalPatterns,"Genus"))
rowData(altExp(GlobalPatterns,"Genus"))$log_mean <-
log(rowData(altExp(GlobalPatterns,"Genus"))$mean)
rowData(altExp(GlobalPatterns,"Genus"))$detected <-
rowData(altExp(GlobalPatterns,"Genus"))$detected / 100
top_taxa <- getTopFeatures(altExp(GlobalPatterns,"Genus"),
method="mean",
top=100L,
assay.type="counts")Colour, size and shape of tree tips and nodes can be decorated based on data
present in the SE object or by providing additional information via the
other_fields argument. Note that currently information for nodes have to be
provided via the other_fields arguments.
Data will be matched via the node or label argument depending on which was
provided. label takes precedent.
plotRowTree(altExp(GlobalPatterns,"Genus")[top_taxa,],
tip_colour_by = "log_mean",
tip_size_by = "detected")
Figure 1: Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size)
Tip and node labels can be shown as well. Setting show_label = TRUE shows the
tip labels only …
plotRowTree(altExp(GlobalPatterns,"Genus")[top_taxa,],
tip_colour_by = "log_mean",
tip_size_by = "detected",
show_label = TRUE)
Figure 2: Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size)
Tip labels of the tree are shown as well.
… whereas node labels can be selectively shown by providing a named logical
vector to show_label.
Please note that currently ggtree can only plot node labels in a rectangular
layout.
labels <- c("Genus:Providencia", "Genus:Morganella", "0.961.60")
plotRowTree(altExp(GlobalPatterns,"Genus")[top_taxa,],
tip_colour_by = "log_mean",
tip_size_by = "detected",
show_label = labels,
layout="rectangular")
Figure 3: Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size)
Selected node and tip labels are shown.
Information can also be visualized on the edges of the tree plot.
plotRowTree(altExp(GlobalPatterns,"Genus")[top_taxa,],
edge_colour_by = "Phylum",
tip_colour_by = "log_mean")
Figure 4: Tree plot using ggtree with tip labels decorated by mean abundance (colour) and edges labeled Kingdom (colour) and prevalence (size)
5 Graph plotting
Similar to tree data, graph data can also be plotted in conjunction with
SummarizedExperiment objects. Since the graph data in itself cannot be stored
in a specialized slot, a graph object can be provided separately or as an
element from the metedata.
Here we load an example graph. As graph data, all objects types accepted by
as_tbl_graph from the tidygraph package are supported.
data(col_graph)In the following examples, the weight data is automatically generated from the
graph data. The SummarizedExperiment provided is required to have overlapping
rownames with the node names of the graph. Using this link the graph plot
can incorporate data from the SummarizedExperiment.
plotColGraph(col_graph,
altExp(GlobalPatterns,"Genus"),
colour_by = "SampleType",
edge_colour_by = "weight",
edge_width_by = "weight",
show_label = TRUE)
#> This graph was created by an old(er) igraph version.
#> Call upgrade_graph() on it to use with the current igraph version
#> For now we convert it on the fly...
As mentioned the graph data can be provided from the metadata of the
SummarizedExperiment.
metadata(altExp(GlobalPatterns,"Genus"))$graph <- col_graphThis produces the same plot as shown above.
6 Plotting of serial data
if(!requireNamespace("devtools", quietly = TRUE)){
BiocManager::install("devtools")
}
if(!requireNamespace("miaTime", quietly = TRUE)){
devtools::install_github("microbiome/miaTime", upgrade = "never")
}# Load data from miaTime package
library("miaTime")
data(SilvermanAGutData, package="miaTime")
tse <- SilvermanAGutData
tse <- transformAssay(tse, method = "relabundance")
taxa <- getTopFeatures(tse, 2)Data from samples collected along time can be visualized using plotSeries.
The x argument is used to reference data from the colData to use as
descriptor for ordering the data. The y argument selects the feature to show.
Since plotting a lot of features is not advised a maximum of 20 features can
plotted at the same time.
plotSeries(tse,
x = "DAY_ORDER",
y = taxa,
colour_by = "Family")If replicated data is present, data is automatically used for calculation of the
mean and sd and plotted as a range. Data from different assays can be used
for plotting via the assay.type.
plotSeries(tse[taxa,],
x = "DAY_ORDER",
colour_by = "Family",
linetype_by = "Phylum",
assay.type = "relabundance")Additional variables can be used to modify line type aesthetics.
plotSeries(tse,
x = "DAY_ORDER",
y = getTopFeatures(tse, 5),
colour_by = "Family",
linetype_by = "Phylum",
assay.type = "counts")7 Plotting factor data
To visualize the relative relations between two groupings among the factor data,
two functions are available for the purpose; plotColTile and plotRowTile.
data(GlobalPatterns, package="mia")
se <- GlobalPatterns
plotColTile(se,"SampleType","Primer") +
theme(axis.text.x.top = element_text(angle = 45, hjust = 0))
8 DMN fit plotting
Searching for groups that are similar to each other among the samples, could be
approached with the Dirichlet Multinomial Mixtures (Holmes, Harris, and Quince 2012).
After using runDMN from the mia package, several k values as a number of clusters
are used to observe the best fit (see also getDMN and getBestDMNFit).
To visualize the fit using e.g. “laplace” as a measure of goodness of fit:
data(dmn_se, package = "mia")
names(metadata(dmn_se))
#> [1] "DMN"
# plot the fit
plotDMNFit(dmn_se, type = "laplace")
#> Warning in .local(x, name, type, ...): 'getDMN' is deprecated.
#> Use 'cluster' instead.
#> See help("Deprecated") and help("Now runDMN and calculateDMN are deprecated. Use cluster with DMMParam parameter and full parameter set as true instead.-deprecated").
9 Serial data ordination and trajectories
Principal Coordinates Analysis using Bray-Curtis dissimilarity on the
hitchip1006 dataset:
library(miaTime)
data(hitchip1006, package = "miaTime")
tse <- hitchip1006
tse <- transformAssay(tse, method = "relabundance")
## Ordination with PCoA with Bray-Curtis dissimilarity
tse <- runMDS(tse, FUN = vegan::vegdist, method = "bray", name = "PCoA_BC",
assay.type = "relabundance", na.rm = TRUE)
# plot
p <- plotReducedDim(tse, dimred = "PCoA_BC")
pRetrieving information about all available trajectories:
library(dplyr)
# List subjects with two time points
selected.subjects <- names(which(table(tse$subject)==2))
# Subjects counts per number of time points available in the data
table(table(tse$subject)) %>% as.data.frame() %>%
rename(Timepoints=Var1, Subjects=Freq)Lets look at all trajectories having two time points in the data:
# plot
p + geom_path(aes(x=X1, y=X2, group=subject),
arrow=arrow(length = unit(0.1, "inches")),
# combining ordination data and metadata then selecting the subjects
# Note, scuttle::makePerCellDF could also be used for the purpose.
data = subset(data.frame(reducedDim(tse), colData(tse)),
subject %in% selected.subjects) %>% arrange(time))+
labs(title = "All trajectories with two time points")+
theme(plot.title = element_text(hjust = 0.5))Filtering the two time point trajectories by divergence and displaying top 10%:
library(miaTime)
# calculating step wise divergence based on the microbial profiles
tse <- getStepwiseDivergence(tse, group = "subject", time_field = "time")
# retrieving the top 10% divergent subjects having two time points
top.selected.subjects <- subset(data.frame(reducedDim(tse), colData(tse)),
subject %in% selected.subjects) %>%
top_frac(0.1, time_divergence) %>% select(subject) %>% .[[1]]
# plot
p + geom_path(aes(x=X1, y=X2,
color=time_divergence, group=subject),
# the data is sorted in descending order in terms of time
# since geom_path will use the first occurring observation
# to color the corresponding segment. Without the sorting
# geom_path will pick up NA values (corresponding to initial time
# points); breaking the example.
data = subset(data.frame(reducedDim(tse), colData(tse)),
subject %in% top.selected.subjects) %>%
arrange(desc(time)),
# arrow end is reversed, due to the earlier sorting.
arrow=arrow(length = unit(0.1, "inches"), ends = "first"))+
labs(title = "Top 10% divergent trajectories from time point one to two")+
scale_color_gradient2(low="white", high="red")+
theme(plot.title = element_text(hjust = 0.5))Plotting an example of the trajectory with the maximum total divergence:
# Get subject with the maximum total divergence
selected.subject <- data.frame(reducedDim(tse), colData(tse)) %>%
group_by(subject) %>%
summarise(total_divergence = sum(time_divergence, na.rm = TRUE)) %>%
filter(total_divergence==max(total_divergence)) %>% select(subject) %>% .[[1]]
# plot
p + geom_path(aes(x=X1, y=X2, group=subject),
data = subset(data.frame(reducedDim(tse), colData(tse)),
subject %in% selected.subject) %>% arrange(time),
arrow=arrow(length = unit(0.1, "inches")))+
labs(title = "Longest trajectory by divergence")+
theme(plot.title = element_text(hjust = 0.5))More examples and materials are available at Orchestrating Microbiome Analysis (Lahti, Shetty, and Ernst 2021).
10 Session info
sessionInfo()
#> R version 4.4.0 beta (2024-04-15 r86425)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] scater_1.32.0 scuttle_1.14.0
#> [3] patchwork_1.2.0 miaViz_1.12.0
#> [5] ggraph_2.2.1 ggplot2_3.5.1
#> [7] mia_1.12.0 MultiAssayExperiment_1.30.0
#> [9] TreeSummarizedExperiment_2.12.0 Biostrings_2.72.0
#> [11] XVector_0.44.0 SingleCellExperiment_1.26.0
#> [13] SummarizedExperiment_1.34.0 Biobase_2.64.0
#> [15] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
#> [17] IRanges_2.38.0 S4Vectors_0.42.0
#> [19] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
#> [21] matrixStats_1.3.0 BiocStyle_2.32.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 jsonlite_1.8.8
#> [3] magrittr_2.0.3 magick_2.8.3
#> [5] ggbeeswarm_0.7.2 farver_2.1.1
#> [7] rmarkdown_2.26 fs_1.6.4
#> [9] zlibbioc_1.50.0 vctrs_0.6.5
#> [11] memoise_2.0.1 DelayedMatrixStats_1.26.0
#> [13] ggtree_3.12.0 tinytex_0.50
#> [15] htmltools_0.5.8.1 S4Arrays_1.4.0
#> [17] BiocNeighbors_1.22.0 SparseArray_1.4.0
#> [19] gridGraphics_0.5-1 sass_0.4.9
#> [21] bslib_0.7.0 plyr_1.8.9
#> [23] DECIPHER_3.0.0 cachem_1.0.8
#> [25] igraph_2.0.3 lifecycle_1.0.4
#> [27] pkgconfig_2.0.3 rsvd_1.0.5
#> [29] Matrix_1.7-0 R6_2.5.1
#> [31] fastmap_1.1.1 GenomeInfoDbData_1.2.12
#> [33] digest_0.6.35 aplot_0.2.2
#> [35] colorspace_2.1-0 ggnewscale_0.4.10
#> [37] irlba_2.3.5.1 vegan_2.6-4
#> [39] beachmat_2.20.0 labeling_0.4.3
#> [41] fansi_1.0.6 httr_1.4.7
#> [43] polyclip_1.10-6 abind_1.4-5
#> [45] mgcv_1.9-1 compiler_4.4.0
#> [47] withr_3.0.0 BiocParallel_1.38.0
#> [49] viridis_0.6.5 DBI_1.2.2
#> [51] highr_0.10 ggforce_0.4.2
#> [53] MASS_7.3-60.2 DelayedArray_0.30.0
#> [55] bluster_1.14.0 permute_0.9-7
#> [57] tools_4.4.0 vipor_0.4.7
#> [59] beeswarm_0.4.0 ape_5.8
#> [61] glue_1.7.0 nlme_3.1-164
#> [63] grid_4.4.0 cluster_2.1.6
#> [65] reshape2_1.4.4 generics_0.1.3
#> [67] gtable_0.3.5 tidyr_1.3.1
#> [69] BiocSingular_1.20.0 tidygraph_1.3.1
#> [71] ScaledMatrix_1.12.0 utf8_1.2.4
#> [73] ggrepel_0.9.5 pillar_1.9.0
#> [75] stringr_1.5.1 yulab.utils_0.1.4
#> [77] splines_4.4.0 dplyr_1.1.4
#> [79] tweenr_2.0.3 treeio_1.28.0
#> [81] lattice_0.22-6 tidyselect_1.2.1
#> [83] DirichletMultinomial_1.46.0 knitr_1.46
#> [85] gridExtra_2.3 bookdown_0.39
#> [87] xfun_0.43 graphlayouts_1.1.1
#> [89] stringi_1.8.3 UCSC.utils_1.0.0
#> [91] lazyeval_0.2.2 ggfun_0.1.4
#> [93] yaml_2.3.8 evaluate_0.23
#> [95] codetools_0.2-20 tibble_3.2.1
#> [97] BiocManager_1.30.22 ggplotify_0.1.2
#> [99] cli_3.6.2 munsell_0.5.1
#> [101] jquerylib_0.1.4 Rcpp_1.0.12
#> [103] parallel_4.4.0 sparseMatrixStats_1.16.0
#> [105] decontam_1.24.0 viridisLite_0.4.2
#> [107] tidytree_0.4.6 scales_1.3.0
#> [109] purrr_1.0.2 crayon_1.5.2
#> [111] rlang_1.1.3 cowplot_1.1.3References
Holmes, Ian, Keith Harris, and Christopher Quince. 2012. “Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics.” PLOS ONE. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0030126.
Lahti, Leo, Sudarshan Shetty, and Felix GM Ernst. 2021. “Orchestrating Microbiome Analysis.” https://microbiome.github.io/OMA/.