inst/doc/animalcules.R

In animalcules: Interactive microbiome analysis toolkit

## ---- include=FALSE-----------------------------------------------------------
knitr::opts_chunk$set(comment="#", message=FALSE)
devtools::load_all(".")
library(SummarizedExperiment)

## ----get_package, eval=FALSE--------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly=TRUE))
#    install.packages("BiocManager")
#  BiocManager::install("compbiomed/animalcules")

## ---- eval=FALSE--------------------------------------------------------------
#  if (!requireNamespace("devtools", quietly=TRUE))
#    install.packages("devtools")
#  devtools::install_github("compbiomed/animalcules")

## ----load, eval=FALSE---------------------------------------------------------
#  library(animalcules)
#  library(SummarizedExperiment)

## ---- eval=FALSE--------------------------------------------------------------
#  run_animalcules()

## -----------------------------------------------------------------------------
data_dir = system.file("extdata/MAE.rds", package = "animalcules")
MAE = readRDS(data_dir)

## ---- eval=FALSE--------------------------------------------------------------
#  data_dir = "PATH_TO_THE_ANIMALCULES_FILE"
#  MAE = readRDS(data_dir)

## -----------------------------------------------------------------------------
p <- filter_summary_pie_box(MAE,
                            samples_discard = c("subject_2", "subject_4"),
                            filter_type = "By Metadata",
                            sample_condition = "AGE")
p

## -----------------------------------------------------------------------------
p <- filter_summary_bar_density(MAE,
                                samples_discard = c("subject_2", "subject_4"),
                                filter_type = "By Metadata",
                                sample_condition = "SEX")
p

## -----------------------------------------------------------------------------
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$sam_table)
result$plot.unbinned
result$plot.binned

## -----------------------------------------------------------------------------
p <- relabu_barplot(MAE,
                    tax_level="family",
                    order_organisms=c('Retroviridae'),
                    sort_by="organisms",
                    sample_conditions=c('SEX', 'AGE'),
                    show_legend=TRUE)
p

## -----------------------------------------------------------------------------
p <- relabu_heatmap(MAE,
                   tax_level="genus",
                   sort_by="conditions",
                   sample_conditions=c("SEX", "AGE"))
p

## -----------------------------------------------------------------------------
p <- relabu_boxplot(MAE,
                    tax_level="genus",
                    organisms=c("Escherichia", "Actinomyces"),
                    condition="SEX",
                    datatype="logcpm")
p

## -----------------------------------------------------------------------------
alpha_div_boxplot(MAE = MAE,
                  tax_level = "genus",
                  condition = "DISEASE",
                  alpha_metric = "shannon")

## -----------------------------------------------------------------------------
do_alpha_div_test(MAE = MAE,
                  tax_level = "genus",
                  condition = "DISEASE",
                  alpha_metric = "shannon",
                  alpha_stat = "T-test")

## -----------------------------------------------------------------------------
diversity_beta_heatmap(MAE = MAE, 
                       tax_level = 'genus', 
                       input_beta_method = "bray",
                       input_bdhm_select_conditions = 'DISEASE',
                       input_bdhm_sort_by = 'condition')

## -----------------------------------------------------------------------------
diversity_beta_boxplot(MAE = MAE, 
                       tax_level = 'genus', 
                       input_beta_method = "bray",
                       input_select_beta_condition = 'DISEASE')

## -----------------------------------------------------------------------------
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)

## -----------------------------------------------------------------------------
result <- dimred_pca(MAE,
                     tax_level="genus",
                     color="AGE",
                     shape="DISEASE",
                     pcx=1,
                     pcy=2,
                     datatype="logcpm")
result$plot
head(result$table)

## -----------------------------------------------------------------------------
result <- dimred_pcoa(MAE,
                      tax_level="genus",
                      color="AGE",
                      shape="DISEASE",
                      axx=1,
                      axy=2,
                      method="bray")
result$plot
head(result$table)

## -----------------------------------------------------------------------------
result <- dimred_umap(MAE,
                      tax_level="genus",
                      color="AGE",
                      shape="DISEASE",
                      cx=1,
                      cy=2,
                      n_neighbors=15,
                      metric="euclidean",
                      datatype="logcpm")
result$plot

## -----------------------------------------------------------------------------
result <- dimred_tsne(MAE,
                      tax_level="phylum",
                      color="AGE",
                      shape="GROUP",
                      k="3D",
                      initial_dims=30,
                      perplexity=10,
                      datatype="logcpm")
result$plot

## -----------------------------------------------------------------------------
p <- differential_abundance(MAE,
                            tax_level="phylum",
                            input_da_condition=c("DISEASE"),
                            min_num_filter = 2,
                            input_da_padj_cutoff = 0.5)
p

## -----------------------------------------------------------------------------
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

# importance plot
p$importance_plot

# ROC plot
p$roc_plot

## -----------------------------------------------------------------------------
sessionInfo()