## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
knitr::opts_chunk$set(dev = "png", dev.args = list(type = "cairo-png")) 

## -----------------------------------------------------------------------------
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("miaViz")

## ----setup, message=FALSE-----------------------------------------------------
library(miaViz)
data(GlobalPatterns, package = "mia")

## -----------------------------------------------------------------------------
plotAbundance(GlobalPatterns, rank = NULL, 
              features = "549322", assay.type = "counts")

## -----------------------------------------------------------------------------
GlobalPatterns <- transformAssay(GlobalPatterns, method = "relabundance")

## -----------------------------------------------------------------------------
plotAbundance(GlobalPatterns, rank = "Kingdom", assay.type = "relabundance")

## -----------------------------------------------------------------------------
prev_phylum <- getPrevalentTaxa(GlobalPatterns, rank = "Phylum",
                                detection = 0.01)

## -----------------------------------------------------------------------------
plotAbundance(GlobalPatterns[rowData(GlobalPatterns)$Phylum %in% prev_phylum],
              rank = "Phylum",
              assay.type = "relabundance")

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

## -----------------------------------------------------------------------------
plotTaxaPrevalence(GlobalPatterns, rank = "Phylum",
                   detections = c(0, 0.001, 0.01, 0.1, 0.2))

## -----------------------------------------------------------------------------
plotPrevalentAbundance(GlobalPatterns, rank = "Family",
                       colour_by = "Phylum") +
    scale_x_log10()

## -----------------------------------------------------------------------------
plotPrevalence(GlobalPatterns,
               rank = "Phylum",
               detections = c(0.01, 0.1, 1, 2, 5, 10, 20)/100,
               prevalences = seq(0.1, 1, 0.1))

## ----message=FALSE------------------------------------------------------------
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 <- getTopTaxa(altExp(GlobalPatterns,"Genus"),
                       method="mean",
                       top=100L,
                       assay.type="counts")

## ----plot1, fig.cap="Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size)"----
plotRowTree(altExp(GlobalPatterns,"Genus")[top_taxa,],
            tip_colour_by = "log_mean",
            tip_size_by = "detected")

## ----plot2, fig.cap="Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size). Tip labels of the tree are shown as well."----
plotRowTree(altExp(GlobalPatterns,"Genus")[top_taxa,],
            tip_colour_by = "log_mean",
            tip_size_by = "detected",
            show_label = TRUE)

## ----plot3, fig.cap="Tree plot using ggtree with tip labels decorated by mean abundance (colour) and prevalence (size). Selected node and tip labels are shown."----
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")

## ----plot4, fig.cap="Tree plot using ggtree with tip labels decorated by mean abundance (colour) and edges labeled Kingdom (colour) and prevalence (size)"----
plotRowTree(altExp(GlobalPatterns,"Genus")[top_taxa,],
            edge_colour_by = "Phylum",
            tip_colour_by = "log_mean")

## -----------------------------------------------------------------------------
data(col_graph)

## -----------------------------------------------------------------------------
plotColGraph(col_graph,
             altExp(GlobalPatterns,"Genus"),
             colour_by = "SampleType",
             edge_colour_by = "weight",
             edge_width_by = "weight",
             show_label = TRUE)

## -----------------------------------------------------------------------------
metadata(altExp(GlobalPatterns,"Genus"))$graph <- col_graph

## ----include=FALSE------------------------------------------------------------
plotColGraph(altExp(GlobalPatterns,"Genus"),
             name = "graph",
             colour_by = "SampleType",
             edge_colour_by = "weight",
             edge_width_by = "weight",
             show_label = TRUE)

## ----eval=FALSE---------------------------------------------------------------
#  if(!requireNamespace("devtools", quietly = TRUE)){
#      BiocManager::install("devtools")
#  }
#  if(!requireNamespace("miaTime", quietly = TRUE)){
#      devtools::install_github("microbiome/miaTime", upgrade = "never")
#  }

## ----eval=FALSE---------------------------------------------------------------
#  # Load data from miaTime package
#  library("miaTime")
#  data(SilvermanAGutData, package="miaTime")
#  tse <- SilvermanAGutData
#  tse <- transformAssay(tse, method = "relabundance")
#  taxa <- getTopTaxa(tse, 2)

## ----eval=FALSE---------------------------------------------------------------
#  plotSeries(tse,
#             x = "DAY_ORDER",
#             y = taxa,
#             colour_by = "Family")

## ----eval=FALSE---------------------------------------------------------------
#  plotSeries(tse[taxa,],
#             x = "DAY_ORDER",
#             colour_by = "Family",
#             linetype_by = "Phylum",
#             assay.type = "relabundance")

## ----eval=FALSE---------------------------------------------------------------
#  plotSeries(tse,
#             x = "DAY_ORDER",
#             y = getTopTaxa(tse, 5),
#             colour_by = "Family",
#             linetype_by = "Phylum",
#             assay.type = "counts")

## -----------------------------------------------------------------------------
data(GlobalPatterns, package="mia")
se <- GlobalPatterns
plotColTile(se,"SampleType","Primer") +
  theme(axis.text.x.top = element_text(angle = 45, hjust = 0))

## -----------------------------------------------------------------------------
data(dmn_se, package = "mia")
names(metadata(dmn_se))
# plot the fit
plotDMNFit(dmn_se, type = "laplace")

## ----MDS, eval=FALSE----------------------------------------------------------
#  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")
#  p

## ----eval=FALSE---------------------------------------------------------------
#  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)

## ----eval=FALSE---------------------------------------------------------------
#  # 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))

## ----eval=FALSE---------------------------------------------------------------
#  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))

## ----eval=FALSE---------------------------------------------------------------
#  # 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))

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

