animalcules
May 11, 2019
Package
animalcules 1.0.1
Contents
1 Introduction
animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights.
2 Installation
Download the package from Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("animalcules")Or install the development version of the package from Github.
BiocManager::install("compbiomed/animalcules")Load the package into R session.
library(animalcules)
library(SummarizedExperiment)3 Load Toy Dataset
This toy dataset contains 50 simulated samples. Throughout the vignette, we will use the data structure MultiAssayExperiment (MAE) as the input to all functions.
data_dir = system.file("extdata/MAE.rds", package = "animalcules")
MAE = readRDS(data_dir)4 Run Shiny App
To run the shiny app, just call: run_animalcules(). The rest of the vignette is for the command line version of animalcules.
run_animalcules()5 Summary and Categorize
One of the first steps in the data analysis process involves summarizing the data and looking for outliers or other obvious data issues that may cause issue with downstream analysis.
5.1 Summary Plot Top
One type of summarization plot returns either a box plot or pie chart for continous or categorical data respectively.
p <- filter_summary_top(MAE,
samples_discard = c("subject_2", "subject_4"),
filter_type = "By Metadata",
sample_condition = "AGE")
p5.2 Summary Plot Bottom
One type of summarization plot returns either a density plot or bar plot for continous or categorical data respectively.
p <- filter_summary_bottom(MAE,
samples_discard = c("subject_2", "subject_4"),
filter_type = "By Metadata",
sample_condition = "SEX")
p5.3 Categorize
It is often necessary to bin continuous data into categories when performing analyses that require categorical input. To help ease this process, users can automatically categorize categorical data and provide custom bin breaks and labels in doing so.
microbe <- MAE[['MicrobeGenetics']]
samples <- as.data.frame(colData(microbe))
result <- filter_categorize(samples,
sample_condition="AGE",
new_label="AGE_GROUP",
bin_breaks=c(0,55,75,100),
bin_labels=c('Young','Adult',"Elderly"))
head(result$sam_table)# AGE SEX DISEASE GROUP AGE_GROUP
# subject_1 34 Female positive A Young
# subject_2 61 Male positive A Adult
# subject_3 62 Male positive A Adult
# subject_4 95 Female positive B Elderly
# subject_5 30 Female positive A Young
# subject_6 80 Female positive B Elderlyresult$plot.unbinnedresult$plot.binned6 Visualization
A typical analysis involves visualization of microbe abundances across samples or groups of samples. Animalcules implements three common types of visualization plots including stacked bar plots, heat map, and box plots generated with Plotly.
6.1 Relative Abundance Stacked Bar Plot
The stacked bar plots are used to visualize the relative abundance of microbes at a given taxonomical level in each sample represented as a single bar.
p <- relabu_barplot(MAE,
tax_level="family",
order_organisms=c('Retroviridae'),
sort_by="organisms",
sample_conditions=c('SEX', 'AGE'),
show_legend=TRUE)
p6.2 Relative Abundance Heatmap
The heatmap represents a sample by organisms matrix that can be visualized at different taxonomic levels.
p <- relabu_heatmap(MAE,
tax_level="genus",
sort_by="conditions",
sample_conditions=c("SEX", "AGE"))
p6.3 Relative Abundance Boxplot
The boxplot visualization allows users to compare the abundance of one or more organisms between categorical attributes.
p <- relabu_boxplot(MAE,
tax_level="genus",
organisms=c("Escherichia", "Actinomyces"),
condition="SEX",
datatype="logcpm")
p# Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
# Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels# Warning: 'layout' objects don't have these attributes: 'boxmode'
# Valid attributes include:
# 'font', 'title', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'colorscale', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'metasrc', 'barmode', 'bargap', 'mapType'7 Diversity
7.1 Alpha Diversity Boxplot
Alpha diversity, which describes the richness and evenness of sample microbial community, is a vital indicator in the microbiome analysis. animalcules provides the interactive boxplot comparison of alpha diversity between selected groups of samples. Both taxonomy levels and alpha diversity metrics (Shannon, Gini Simpson, Inverse Simpson) can be changed. Users can also conduct alpha diversity statistical tests including Wilcoxon rank sum test, T test and Kruskal-Wallis test.
Plot the alpha diversity boxplot between the levels in selected condition.
alpha_div_boxplot(MAE = MAE,
tax_level = "genus",
condition = "DISEASE",
alpha_metric = "shannon")7.2 Alpha Diversity Statistical Test
Conduct statistical test on the alpha diversity between the levels in selected condition.
do_alpha_div_test(MAE = MAE,
tax_level = "genus",
condition = "DISEASE",
alpha_metric = "shannon",
alpha_stat = "T-test")# Wilcoxon rank sum test Welch Two Sample t-test
# P-value 0.3667155 0.48796327.3 Beta Diversity Heatmap
On the other hand, by defining distances between each sample, beta diversity is another key metric to look at. Users can plot the beta diversity heatmap by selecting different beta diversity dissimilarity metrics including Bray-Curtis and Jaccard. Users can also conduct beta diversity statistical testing between groups including PERMANOVA, Wilcoxon rank sum test and Kruskal-Wallis.
Plot the beta diversity heatmap with selected condition.
diversity_beta_heatmap(MAE = MAE,
tax_level = 'genus',
input_beta_method = "bray",
input_bdhm_select_conditions = 'DISEASE',
input_bdhm_sort_by = 'condition')7.4 Beta Diversity Boxplot
Plot the beta diversity boxplot within and between conditions.
diversity_beta_boxplot(MAE = MAE,
tax_level = 'genus',
input_beta_method = "bray",
input_select_beta_condition = 'DISEASE')7.5 Beta Diversity Test
Conduct statistical test on the beta diversity between the levels in selected condition.
diversity_beta_test(MAE = MAE,
tax_level = 'genus',
input_beta_method = "bray",
input_select_beta_condition = 'DISEASE',
input_select_beta_stat_method = 'PERMANOVA',
input_num_permutation_permanova = 999)# Permutation test for adonis under reduced model
# Terms added sequentially (first to last)
# Permutation: free
# Number of permutations: 999
#
# vegan::adonis2(formula = dist.mat ~ condition, data = sam_table, permutations = input_num_permutation_permanova, strata = "PLOT")
# Df SumOfSqs R2 F Pr(>F)
# condition 1 0.2066 0.01495 0.7283 0.652
# Residual 48 13.6149 0.98505
# Total 49 13.8215 1.000008 Dimensionality Reduction
8.1 PCA
A wrapper for conduction 2D and 3D Principal Component Analysis.
result <- dimred_pca(MAE,
tax_level="genus",
color="AGE",
shape="DISEASE",
pcx=1,
pcy=2,
datatype="logcpm")
result$plothead(result$table)# PC Standard Deviation Variance Explained Cumulative Variance
# PC1 PC1 2.5599634292793 10.4% 10.4%
# PC2 PC2 2.19272108550489 7.6% 18.0%
# PC3 PC3 2.00513673550525 6.4% 24.4%
# PC4 PC4 1.87545892419787 5.6% 30.0%
# PC5 PC5 1.80253703972772 5.2% 35.2%
# PC6 PC6 1.70630724920304 4.6% 39.8%8.2 PCoA
A wrapper for conduction 2D and 3D Principal Coordinate Analysis.
result <- dimred_pcoa(MAE,
tax_level="genus",
color="AGE",
shape="DISEASE",
axx=1,
axy=2,
method="bray")
result$plothead(result$table)# Axis Eigenvalue Variance Explained Cumulative Variance
# Axis.1 Axis.1 3.6608829 21.0% 21.0%
# Axis.2 Axis.2 2.7032256 15.6% 36.5%
# Axis.3 Axis.3 1.6483657 9.7% 46.2%
# Axis.4 Axis.4 0.8576656 5.3% 51.5%
# Axis.5 Axis.5 0.7871770 4.9% 56.4%
# Axis.6 Axis.6 0.7508249 4.7% 61.0%8.3 t-SNE
A wrapper for conduction 2D and 3D t-distributed stochastic neighbor embedding.
result <- dimred_tsne(MAE,
tax_level="phylum",
color="AGE",
shape="GROUP",
k="3D",
initial_dims=30,
perplexity=10,
datatype="logcpm")
result$plot9 Differential Analysis
Here in animalcules, we provide a DESeq2-based differential abundance analysis. Users can choose the target variable, covariate variable, taxonomy level, minimum count cut-off, and an adjusted p-value threshold. The analysis report will output not only the adjusted p-value and log2-fold-change of the microbes, but also the percentage, prevalence, and the group size-adjusted fold change.
p <- differential_abundance(MAE,
tax_level="phylum",
input_da_condition=c("DISEASE"),
min_num_filter = 2,
input_da_padj_cutoff = 0.5)
p# microbe padj pValue log2FoldChange positive negative prevalence
# 1 Actinobacteria 0.206 0.0716 0.704 28/28 22/22 100.00%
# 2 Ascomycota 0.206 0.1030 -0.372 28/28 22/22 100.00%
# 3 Basidiomycota 0.206 0.0874 -1.460 19/28 15/22 68.00%
# 4 Firmicutes 0.206 0.0850 0.817 28/28 22/22 100.00%
# 5 others 0.206 0.0783 -0.479 28/28 22/22 100.00%
# 6 Proteobacteria 0.260 0.1560 -0.649 28/28 22/22 100.00%
# Group Size adjusted fold change
# 1 1
# 2 1
# 3 1
# 4 1
# 5 1
# 6 110 Biomarker
One unique feature of animalcules is the biomarker identification module built on machine learning models. Users can choose one classification model from logistic regression, gradient boosting machine, or random forest to identify a microbes biomarker list. The feature importance score for each microbe will be provided. To evaluate the biomarker performance, the ROC plot and the AUC value using cross-validation outputs are shown to users. Note: Results may vary each run.
10.1 Train biomarker
p <- find_biomarker(MAE,
tax_level = "genus",
input_select_target_biomarker = c("SEX"),
nfolds = 3,
nrepeats = 3,
seed = 99,
percent_top_biomarker = 0.2,
model_name = "logistic regression")
# biomarker
p$biomarker# biomarker_list
# 1 Treponema
# 2 Veillonella
# 3 Dialister
# 4 Neisseria
# 5 Hepacivirus
# 6 Alphapapillomavirus
# 7 Deinococcus
# 8 Haemophilus
# 9 Aspergillus
# 10 Escherichia
# 11 Xanthomonas
# 12 Cupriavidus
# 13 Malassezia# importance plot
p$importance_plot
# ROC plot
p$roc_plot
11 Session Info
sessionInfo()# R version 3.6.0 (2019-04-26)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows Server 2012 R2 x64 (build 9600)
#
# Matrix products: default
#
# locale:
# [1] LC_COLLATE=C
# [2] LC_CTYPE=English_United States.1252
# [3] LC_MONETARY=English_United States.1252
# [4] LC_NUMERIC=C
# [5] LC_TIME=English_United States.1252
#
# attached base packages:
# [1] grid parallel stats4 stats graphics grDevices utils
# [8] datasets methods base
#
# other attached packages:
# [1] DMwR_0.4.1 caret_6.0-84
# [3] ggplot2_3.1.1 lattice_0.20-38
# [5] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
# [7] BiocParallel_1.18.0 matrixStats_0.54.0
# [9] Biobase_2.44.0 GenomicRanges_1.36.0
# [11] GenomeInfoDb_1.20.0 IRanges_2.18.0
# [13] S4Vectors_0.22.0 BiocGenerics_0.30.0
# [15] animalcules_1.0.1 BiocStyle_2.12.0
#
# loaded via a namespace (and not attached):
# [1] backports_1.1.4 Hmisc_4.2-0
# [3] plyr_1.8.4 lazyeval_0.2.2
# [5] splines_3.6.0 crosstalk_1.0.0
# [7] digest_0.6.18 foreach_1.4.4
# [9] htmltools_0.3.6 gdata_2.18.0
# [11] magrittr_1.5 checkmate_1.9.3
# [13] memoise_1.1.0 covr_3.2.1
# [15] cluster_2.0.9 ROCR_1.0-7
# [17] limma_3.40.0 recipes_0.1.5
# [19] annotate_1.62.0 gower_0.2.0
# [21] xts_0.11-2 colorspace_1.4-1
# [23] blob_1.1.1 xfun_0.6
# [25] dplyr_0.8.0.1 crayon_1.3.4
# [27] RCurl_1.95-4.12 jsonlite_1.6
# [29] genefilter_1.66.0 survival_2.44-1.1
# [31] zoo_1.8-5 iterators_1.0.10
# [33] ape_5.3 glue_1.3.1
# [35] gtable_0.3.0 ipred_0.9-9
# [37] zlibbioc_1.30.0 XVector_0.24.0
# [39] quantmod_0.4-14 rentrez_1.2.2
# [41] abind_1.4-5 scales_1.0.0
# [43] DBI_1.0.0 Rcpp_1.0.1
# [45] plotROC_2.2.1 viridisLite_0.3.0
# [47] xtable_1.8-4 htmlTable_1.13.1
# [49] foreign_0.8-71 bit_1.1-14
# [51] Formula_1.2-3 tsne_0.1-3
# [53] lava_1.6.5 prodlim_2018.04.18
# [55] DT_0.6 glmnet_2.0-16
# [57] htmlwidgets_1.3 rex_1.1.2
# [59] httr_1.4.0 gplots_3.0.1.1
# [61] RColorBrewer_1.1-2 acepack_1.4.1
# [63] pkgconfig_2.0.2 XML_3.98-1.19
# [65] nnet_7.3-12 locfit_1.5-9.1
# [67] tidyselect_0.2.5 labeling_0.3
# [69] rlang_0.3.4 reshape2_1.4.3
# [71] later_0.8.0 AnnotationDbi_1.46.0
# [73] munsell_0.5.0 tools_3.6.0
# [75] generics_0.0.2 RSQLite_2.1.1
# [77] evaluate_0.13 stringr_1.4.0
# [79] yaml_2.2.0 ModelMetrics_1.2.2
# [81] knitr_1.22 bit64_0.9-7
# [83] caTools_1.17.1.2 purrr_0.3.2
# [85] nlme_3.1-139 mime_0.6
# [87] compiler_3.6.0 rstudioapi_0.10
# [89] plotly_4.9.0 curl_3.3
# [91] tibble_2.1.1 geneplotter_1.62.0
# [93] stringi_1.4.3 forcats_0.4.0
# [95] Matrix_1.2-17 permute_0.9-5
# [97] vegan_2.5-4 shinyjs_1.0
# [99] pillar_1.3.1 BiocManager_1.30.4
# [101] data.table_1.12.2 bitops_1.0-6
# [103] httpuv_1.5.1 R6_2.4.0
# [105] latticeExtra_0.6-28 bookdown_0.10
# [107] promises_1.0.1 KernSmooth_2.23-15
# [109] gridExtra_2.3 codetools_0.2-16
# [111] MASS_7.3-51.4 gtools_3.8.1
# [113] assertthat_0.2.1 DESeq2_1.24.0
# [115] withr_2.1.2 GenomeInfoDbData_1.2.1
# [117] mgcv_1.8-28 MultiAssayExperiment_1.10.0
# [119] rpart_4.1-15 timeDate_3043.102
# [121] tidyr_0.8.3 class_7.3-15
# [123] rmarkdown_1.12 Cairo_1.5-10
# [125] TTR_0.23-4 shiny_1.3.2
# [127] lubridate_1.7.4 base64enc_0.1-3