---
output:
  html_document:
    toc: true                               # table of contents
    toc_float: true                         # float the table of contents to the left of the main document content
    toc_depth: 4                            # header levels 1,2,3
    theme: default
    number_sections: false                  # add section numbering to headers
    df_print: paged                         # tables are printed as an html table with support for pagination over rows and columns
    highlight: pygments
    pdf_document: true
  pdf_document:
    toc: yes
date: "`r Sys.Date()`"
params:
  meta: NULL
  input_dir: NULL
  artifact_dir: NULL
  cpus: 1 
  study_type: NULL
  study_name: NULL
  observations_type: NULL
  observations_file: NULL                                          # GSE156533.samplesheet.csv
  observations_id_col: NULL
  features_file: NULL
  features_type: NULL
  features_id_col: NULL
  features_name_col: NULL
  features_metadata_cols: 
    - gene_id
    - gene_name
    - gene_biotype
  raw_matrix: null                                            # e.g. 0_salmon.merged.gene_counts.tsv
  normalised_matrix: null
  variance_stabilised_matrix: null                            # e.g. test_files/3_treatment-WT-P23H.vst.tsv
  contrasts_file: null                                        # e.g. GSE156533.contrasts.csv
  differential_table: file.csv
  exploratory_n_features: null
  exploratory_clustering_method: null
  exploratory_cor_method: null
  exploratory_whisker_distance: null
  exploratory_mad_threshold: null
  exploratory_main_variable: null
  versions_file: null                                         # e.g 17_software_versions.yml
  logo: null
  css: null
  citations: null
  filtering_min_samples: 1
  filtering_min_abundance: 1
  filtering_min_proportion: NULL
  filtering_grouping_var: NULL
  differential_file_suffix: NULL
  differential_feature_id_column: NULL
  differential_feature_name_column: NULL
  differential_fc_column: NULL
  differential_pval_column: NULL
  differential_qval_column: NULL
  differential_min_fold_change: NULL
  differential_foldchanges_logged: NULL
  differential_max_qval: NULL
  deseq2_test: NULL
  deseq2_fit_type: NULL
  deseq2_sf_type: NULL
  deseq2_min_replicates_for_replace: NULL
  deseq2_use_t: NULL
  deseq2_lfc_threshold: NULL
  deseq2_alt_hypothesis: NULL
  deseq2_independent_filtering: NULL
  deseq2_p_adjust_method: NULL
  deseq2_alpha: NULL
  deseq2_minmu: NULL
  deseq2_vs_method: NULL
  deseq2_shrink_lfc: NULL
  deseq2_cores: NULL
  deseq2_vs_blind: NULL
  deseq2_vst_nsub: NULL
  gsea_run: false
  gsea_nperm: NULL
  gsea_permute: NULL
  gsea_scoring_scheme: NULL    
  gsea_metric: NULL 
  gsea_sort: NULL
  gsea_order: NULL
  gsea_set_max: NULL
  gsea_set_min: NULL
  gsea_norm: NULL
  gsea_rnd_type: NULL
  gsea_make_sets: NULL
  gsea_median: NULL
  gsea_num: NULL
  gsea_plot_top_x: NULL
  gsea_rnd_seed: NULL
  gsea_save_rnd_lists: NULL
  gsea_zip_report: NULL
  gsea_chip_file: NULL 
  gsea_gene_sets: NULL
---

<!-- Load libraries -->

```{r, include=FALSE}
library(knitr)
library(yaml)
library(shinyngs)
library(plotly)
library(DT)
```

```{r include = FALSE}
# Load the datatables js
datatable(NULL)
```

```{r, echo=FALSE}

# this function will be available via shinyngs in a release soon but we can use it here for now
anova_pca_metadata <- function(pca_coords, pcameta, fraction_explained){
  # Use 10 components or however many fewer is produced by the PCA
  
  last_pc <- 10
  if (ncol(pca_coords) < last_pc) {
    last_pc <- ncol(pca_coords)
  }
  
  # Remove non-useful variables (those with 1 value, or N values where N is the
  # number of samples)
  
  pcameta <- pcameta[, chooseGroupingVariables(pcameta), drop = FALSE]
  
  # Run anova for all PCA against the selected meta vars

  run_anova <- function(meta_col, pc){
    fit <- aov(pca_coords[, pc] ~ factor(pcameta[, meta_col]))
    smry <- summary(fit)[[1]]

    if ("Pr(>F)" %in% names(smry)) {
      smry[["Pr(>F)"]][[1]]
    }else{
      NA
    }
  }
  
  pvals <- outer(
    1:ncol(pcameta), 
    1:last_pc,
    Vectorize(run_anova)  
  )
  
  # Name dimensions
  
  dimnames(pvals) <- list(
    colnames(pcameta),
    paste(
      paste("PC", 1:last_pc, sep = ""),
      " (",
      fraction_explained[1:last_pc],
      "%)",
      sep = ""
    )
  )
  
  pvals
}
```


```{r, include=FALSE}
versions <- unlist(yaml.load_file(file.path(params$input_dir, params$versions_file)), recursive = FALSE)
params_table <- data.frame(Parameter = names(unlist(params)), Value = unlist(params), row.names = NULL)

# We'll subset the params table for different report sections
make_params_table <- function(name, pattern = NULL, remove_pattern = FALSE){
  subparams <- params_table
  if (! is.null(pattern)){
    subparams <- subparams[grep(pattern, subparams$Parameter),]
  }
  if (remove_pattern){
    subparams$Parameter <- sub(pattern, '', subparams$Parameter)
  }
  
  if (nrow(subparams) > 10){
    dom <- 'tp'
  }else{
    dom <- 't'
  }
  
  print( htmltools::tagList(datatable(subparams, caption = paste("Parameters used for", name), rownames = FALSE, options = list(dom = dom)) ))
}

```

---
title:  "<img src=\"`r file.path(params$input_dir, params$logo)`\" style=\"float: left;\"/>Differential `r params$features_type` abundance report"
subtitle: differentialabundance workflow version `r versions[["Workflow.nf-core/differentialabundance"]]`
---

<!-- set notebook defaults -->

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

<!-- Include the CSS and set the logo -->

```{r, echo=FALSE}
htmltools::includeCSS(params$css)
```

```{r results="asis", echo=FALSE}
cat(paste0("
<style>
#TOC {
   background-image: url(\"", knitr::image_uri(params$logo), "\");
}
</style>
"))
```

<!-- Load input data -->

```{r, echo=FALSE}
observations <- read_metadata(file.path(params$input_dir, params$observations_file))
if (! is.null(params$features_file)){
  features <- read_metadata(file.path(params$input_dir, params$features_file))
  features <- features[,colnames(features) %in% params$features_metadata_cols, drop = FALSE]
}

rownames(observations) <- observations[,1]
observations_name_col <- colnames(observations)[1]

contrasts <- read_metadata(file.path(params$input_dir, params$contrasts_file))
contrasts$blocking <- na.replace(contrasts$blocking, '')
if (! 'id' %in% colnames(contrasts)){
  contrasts$id <- apply(contrasts, 1, paste, collapse='_')
}

# Identify informative variables- those with a number of values greater than 1
# but less than N, with N being the number of observations. Make sure contrast
# variables are first in the list

informative_variables <- unique(c(contrasts$variable, chooseGroupingVariables(observations)))

# Remove any informative variables that group observations the same way
informative_variables <- informative_variables[ ! duplicated(lapply(structure(informative_variables, names= informative_variables), function(x) as.numeric(factor(observations[[x]], levels=unique(observations[[x]])))))]

assay_files <- list(
  raw = file.path(params$input_dir, params$raw_matrix),
  normalised = file.path(params$input_dir,params$normalised_matrix),
  variance_stabilised = file.path(params$input_dir, params$variance_stabilised_matrix)
)
assay_files <- Filter(Negate(is.null), assay_files)
assay_data <- lapply(assay_files, function(x) {
  mat <- read_matrix(
    x,
    sample_metadata = observations,
    row.names = 1
  )
  colnames(mat) <- observations[[observations_name_col]][match(colnames(mat), rownames(observations))]
  mat
})

# Now we can rename the observations rows using the title field
rownames(observations) <- observations[[observations_name_col]]

# Run PCA early so we can understand how important each variable is

pca_datas <- lapply(names(assay_data), function(assay_type){
  if (assay_type == 'variance_stabilised'){
    compilePCAData(assay_data[[assay_type]])
  }else{
    compilePCAData(log2(assay_data[[assay_type]]+1))
  }
})
names(pca_datas) <- names(assay_data)

pca_vs_meta <- anova_pca_metadata(pca_datas[['variance_stabilised']]$coords, observations[,informative_variables], pca_datas[['variance_stabilised']]$percentVar)

# Show the variable with the tightest PC associations first
informative_variables <- rownames(pca_vs_meta)[order(pca_vs_meta[,1])]

# Pick the variable used for coloring purposes etc
if (params$exploratory_main_variable == 'contrasts'){
  main_grouping_variable <- contrasts$variable[1]
}else if (params$exploratory_main_variable == 'auto_pca'){
  main_grouping_variable <- informative_variables[1]
}else{
  if (! params$exploratory_main_variable %in% colnames(observations)){
    stop(paste('Invalid main variable specified: ', params$exploratory_main_variable))
  }
  main_grouping_variable <- params$exploratory_main_variable
}

groupColorScale <- makeColorScale(length(unique(observations[[main_grouping_variable]])), palette = "Set1")
```

<!-- Read the differential results.
NOTE: differential results files are expected to have the pattern:

<variable>-<reference>-<target>-<blocking><differential_file_suffix>, e.g.
treatment-mCherry-hND6-batcheffect.deseq2.results.tsv

... where variable, reference, target and blocking come from the contrasts file
(with blocking being optional) and the suffix is defined in parameters.
-->

```{r, echo=FALSE}

prefix_part_names <- c('variable', 'reference', 'target', 'blocking')
diff_prefixes <- sub('-$', '', apply(contrasts[,prefix_part_names], 1, function(x) paste(x, collapse = '-')))

differential_files <- lapply(diff_prefixes, function(d){
 file.path(params$input_dir, paste0(gsub(' ', '_', d), params$differential_file_suffix)) 
})

differential_results <- lapply(differential_files, function(diff_file){
  if (! file.exists(diff_file)){
    stop(paste("Differential file", diff_file, "does not exist"))
  }
  diff <- read_differential(
    diff_file, 
    feature_id_column = params$differential_feature_id_column,
    fc_column = params$differential_fc_column,
    pval_column = params$differential_pval_column,
    qval_column = params$differential_qval_column
  )
  
  # If fold changes are not logged already, log them (we assume they're logged
  # later on)
  
  if (! params$differential_foldchanges_logged){
    diff[[params$differential_fc_column]] <- log2(diff[[params$differential_fc_column]])
  }
  
  # Annotate differential tables if possible
  
  if (! is.null(params$features_file)){
    diff <- merge(features, diff, by = params$differential_feature_id_column)
  }
  diff
})
names(differential_results) <- diff_prefixes
```

<!-- Calculate some summary statistics -->

```{r, echo=FALSE}

contrast_descriptions <- paste(contrasts$target, 'versus', contrasts$reference, 'in', contrasts$variable) 
with_blocking <- which(contrasts$blocking != '')
contrast_descriptions[with_blocking] <- paste0(contrast_descriptions[with_blocking], " (blocking on ", contrasts$blocking[with_blocking],")")

sig_differential <- 
  lapply(
    1:nrow(contrasts),
    function(x){
      signif <- differential_results[[x]][,params$differential_qval_column ] < params$differential_max_qval
      list(
        up = differential_results[[x]][which(
          differential_results[[x]][,params$differential_fc_column ] > log2(params$differential_min_fold_change) &
          signif
        ),],
        down = differential_results[[x]][which(
          differential_results[[x]][,params$differential_fc_column ] < log2(1/params$differential_min_fold_change) &
          signif 
        ),]
      ) 
    })
names(sig_differential) <- contrast_descriptions

# Count the differential genes
differential_table <- do.call(rbind, lapply(sig_differential, function(x) lapply(x, function(y) nrow(y))))
```

<!-- Write the report -->

# Abstract

This report summarises differential `r params$features_type` analysis as performed by the nf-core/differentialabundance pipeline. 

# Data

```{r, echo=FALSE, results='asis'}
cat(paste0("\n## ", ucfirst(params$observations_type), "s\n"))
```


A summary of `r params$observations_type` metadata is below:

```{r, echo=FALSE, results='asis'}
display_columns <- union(c(params$observations_id_col, unique(contrasts$variable)), informative_variables)
minimal_fetchngs_cols <- c('sample', 'sample_title', 'strandedness', 'library_strategy', 'scientific_name')

# If the data came via fetchngs then we can infer a couple of things about the most useful columns

if (all(minimal_fetchngs_cols %in% colnames(observations))){
  additional_useful_cols <- minimal_fetchngs_cols
}else{
  additional_useful_cols <- colnames(observations)[which(apply(observations, 2, function(x) max(nchar(x))) <= 20)] 
}

display_columns <- head(union(display_columns, additional_useful_cols), 5)

# Also add informative columns
display_columns <- unique(c(display_columns, informative_variables))
observations_to_print <- observations[,unique(display_columns)]
colnames(observations_to_print) <- prettifyVariablename(colnames(observations_to_print))
print( htmltools::tagList(datatable(observations_to_print, caption = paste(ucfirst(params$observations_type), 'metadata'), rownames = FALSE, options = list(dom = 't')) ))

```

## Contrasts

Comparisons were made between `r params$observations_type` groups defined using using `r params$observation_type` metadata columns, as described in the following table of contrasts:

```{r, echo=FALSE, results='asis'}
contrasts_to_print <- contrasts
colnames(contrasts_to_print) <- prettifyVariablename(colnames(contrasts_to_print))
print( htmltools::tagList(datatable(contrasts_to_print, caption = paste0("Table of contrasts"), rownames = FALSE, options = list(dom = 't')) ))
```

# Results

## Counts

Input was a matrix of `r nrow(assay_data$raw)` `r params$features_type`s for `r ncol(assay_data$raw)` `r params$observations_type`s`r ifelse(nrow(assay_data$normalised) < nrow(assay_data$raw), paste0(', reduced to ', nrow(assay_data$normalised), ' ', params$features_type, 's after filtering for low abundance'), '')`.

## Exploratory analysis

### Abundance value distributions

The following plots show the abundance value distributions of input matrices. A log2 transformation is applied where not already performed.

```{r, include=FALSE}

```
#### Box plots

```{r, echo=FALSE, results='asis', fig.height=8}

ggplot_boxplot(
  assay_data, 
  experiment = observations, 
  colorby = main_grouping_variable, 
  expressiontype = paste("count per", params$features_type), 
  palette = groupColorScale,
  whisker_distance = params$exploratory_whisker_distance,
  base_size=8
)
```

Whiskers in the above boxplots show `r params$exploratory_whisker_distance` times the inter-quartile range.

#### Density plots

```{r, echo=FALSE, results='asis', fig.height=8}
plotly_densityplot(
  assay_data, 
  experiment = observations, 
  colorby = main_grouping_variable, 
  expressiontype = paste("count per", params$features_type), 
  palette = groupColorScale
)
```

```{r, echo=FALSE, results='asis'}
cat(paste0("\n### ", ucfirst(params$observations_type), " relationships\n"))
```

#### Principal components plots {.tabset}

Principal components analysis was conducted based on the `r params$exploratory_n_features` most variable `r params$features_type`s. Each component was annotated with its percent contribution to variance. 

```{r, echo=FALSE, results='asis'}
for (assay_type in rev(names(assay_data))){
  
  pca_data <- pca_datas[[assay_type]]

  for (iv in informative_variables){
  
    cat(paste0("\n##### ", prettifyVariablename(assay_type), " (", iv, ")\n"))
  
    plotdata <- pca_data$coords
    plotdata$colorby <- factor(
      observations[[iv]], 
      levels = unique(observations[[iv]])
    )

    # Make plotting data combining PCA coords with coloring groups etc

    plotdata$name <- rownames(plotdata)
    percentVar <- pca_data$percentVar
    labels <- paste0(colnames(plotdata), " (", sprintf("%.1f", percentVar), "%)")
    ncats <- length(unique(plotdata$colorby))

    plot_types <- list("2" = "scatter", "3" = "scatter3d")

    for (d in names(plot_types)) {
  
      # Default plot args whatever we're doing

      plot_args <- list(
        x = pca_data$coords[, 1],
        y = pca_data$coords[, 2],
        xlab = labels[1],
        ylab = labels[2],
        colorby = plotdata$colorby,
        plot_type = plot_types[[d]],
        palette = groupColorScale,
        legend_title = prettifyVariablename(iv),
        labels = plotdata$name,
        show_labels = TRUE
      )
    
      if (d == "3") {
        plot_args$z <- pca_data$coords[, 3]
        plot_args$zlab <- labels[3]
      }

      print(htmltools::tagList(do.call("plotly_scatterplot", plot_args)))
    }
  }
}
```

#### Principal components/ metadata associations

For the variance stabilised matrix, an ANOVA test was used to determine assocations between continuous principal components and categorical covariates (including the variable of interest).

The resulting p values are illustrated below.

```{r, echo=FALSE, results='asis'}

# This is a little hack to work around a bug in d3heatmap with single-row data
# frames.
if (nrow(pca_vs_meta) == 1){
  plot_pca_meta <- rbind(pca_vs_meta, pca_vs_meta)
}else{
  plot_pca_meta <- pca_vs_meta
}

d3heatmap::d3heatmap(
  -log10(plot_pca_meta),
  Rowv = FALSE,
  dendrogram = 'none',
  cellnote = plot_pca_meta,
  cexCol = 0.8, 
  cexRow = 0.8,
  height = (100 + (15 * nrow(plot_pca_meta))),
  colors = colorRampPalette(
    rev(
      RColorBrewer::brewer.pal(n = 7, name = "RdYlBu")
    )
  )(100)
)

for (variable in rownames(pca_vs_meta)){
  sig_comps <- pca_vs_meta[variable,] < 0.1

  if (any(sig_comps)){
    min_sig_comp <- min(which(sig_comps))
  
    min_sig_comp_p <- sprintf("%.2f", pca_vs_meta[variable, min_sig_comp])
    cat(paste0('The variable \'', variable, '\' shows an association with ', colnames(pca_vs_meta)[min_sig_comp], ' (p = ', min_sig_comp_p,'). '))
  }
}
```

#### Clustering dendrograms

A hierarchical clustering of `r params$features_type`s was undertaken based on the top  `r params$exploratory_n_features` most variable `r params$features_type`s. Distances between `r params$features_type`s were estimated based on `r params$exploratory_cor_method` correlation, which were then used to produce a clustering via the `r params$exploratory_clustering_method` method with `hclust()` in R. 

```{r, echo=FALSE, results='asis'}
variable_genes <- selectVariableGenes(matrix = assay_data$variance_stabilised, ntop = params$exploratory_n_features)

p <- clusteringDendrogram(
  2^assay_data$variance_stabilised[variable_genes, ],
  observations[, main_grouping_variable, drop = FALSE],
  colorby = main_grouping_variable,
  cor_method = params$exploratory_cor_method,
  plot_title = paste0(
    paste0(params$observations_type," clustering dendrogram, "), 
    params$exploratory_n_features, 
    " most variable ", 
    params$features_type, 
    "s\n(", params$exploratory_clustering_method, " clustering, ", params$exploratory_cor_method, " correlation)"),
  cluster_method = params$exploratory_clustering_method,
  palette = groupColorScale,
  labelspace = 0.25
)

# Defaults in shinyngs make the text in this plot a bit big for the report, so
# scale it down a bit
print(p, vp=grid::viewport(gp=grid::gpar(cex=0.7)))

```

### Outlier detection {.tabset}

Outlier detection based on [median absolute deviation](https://wiki.arrayserver.com/wiki/index.php?title=CorrelationQC.pdf) was undertaken, the outlier scoring is plotted below.

```{r, echo=FALSE, results='asis', warning=FALSE}

# We can't look for ouliers in sets of less than 3 samples, so exclude variables
# unless the minimum group size is larger than that
iv_min_group_sizes <- unlist(lapply(informative_variables, function(x) min(table(observations[[x]]))))

foo <- lapply(informative_variables[iv_min_group_sizes > 2], function(iv){
  
  cat(paste("\n####", iv, "\n"))
  
  plotdata <-
  madScore(
    matrix = assay_data[['variance_stabilised']],
    sample_sheet = observations,
    groupby = iv
  )

  mad_plot_args <- list(
    x = plotdata$group,
    y = plotdata$mad,
    color = plotdata$outlier,
    hline_thresholds = c("Outlier threshold" = params$exploratory_mad_threshold),
    palette = makeColorScale(2, palette = "Set1"),
    legend_title = "Outlier status",
    labels = rownames(plotdata),
    show_labels = TRUE,
    xlab = "Sample group",
    ylab = "MAD score"
  )

  print(htmltools::tagList(do.call("plotly_scatterplot", mad_plot_args)))

  outliers <- rownames(plotdata)[plotdata$outlier]

  if (length(outliers) == 0){
    cat(paste0("No outlying samples were detected in groups defined by ", iv,".\n"))
  }else{
    cat(paste0(length(outliers), ' possible outliers were detected in groups defined by ', iv ,': ', paste(outliers, collapse=', '), "\n"))
  }
})

```

## Differential analysis

### Differential `r params$features_type` counts

```{r, echo=FALSE, results='asis'}
kable(differential_table, caption = paste0('Differential ',  params$features_type, ' counts (target relative to reference)'))
```

```{r, echo=FALSE, results='asis', eval = FALSE}

differential_summary_string <- paste(
  paste(
  lapply(
    1:nrow(contrasts),
    function(x){
      paste0(
        "Contrast ", x, ' (', contrast_descriptions[x], ') ', "had ", differential_table[x,'up'], ' ', paste0(params$features_type, 's'), ' expressed significantly more highly in ', contrasts[x, 'target',], ' than ', contrasts[x, 'reference',], ' and ', differential_table[x,'down'], ' expressed at sifnificantly lower levels.'
      )
    }
  ),
  collapse = ' '
  )
)
cat(differential_summary_string)
```

### Differential `r params$features_type` details

```{r, echo=FALSE, results='asis'}
for (i in 1:nrow(contrasts)){
  cat("\n#### ", contrast_descriptions[i], "  {.tabset}\n")
  
  ## Make a volcano plot for the contrast first
  
  # Label features with symbol as well as identifier
  if (! is.null(params$features_file) && (! is.null(params$differential_feature_name_column)) ){
    label_col <- params$differential_feature_name_column
  }else{
    label_col <- params$differential_feature_id_column
  }

  # Get the full set of differential stats for this contrast, removing rows with
  # NAs in the fields we need.
  full_de <- differential_results[[i]]
  full_de <- subset(full_de, (! is.na(full_de[[params$differential_fc_column]])) & (! is.na(full_de[[params$differential_qval_colum]])) )
  
  # We'll color by whether features are differential according to supplied thresholds

  p_value_types <- list(Adjusted = params$differential_qval_column, Unadjusted = params$differential_pval_column)
  
  for (pvt in names(p_value_types)){
    cat("\n##### ", pvt, " p values\n")
    pval_column <- p_value_types[[pvt]]
  
    full_de$differential_status <- FALSE
    full_de$differential_status[abs(full_de[[params$differential_fc_column]]) > log2(params$differential_min_fold_change) & full_de[[pval_column]] < params$differential_max_qval] <- TRUE

    # Define the thresholds we'll draw

    hline_thresholds = vline_thresholds = list()
    hline_thresholds[[paste(pval_column, '=', params$differential_max_qval)]] = -log10(params$differential_max_qval)
    vline_thresholds[[paste(params$differential_fc_column, '<-', log2(params$differential_min_fold_change))]] = -log2(params$differential_min_fold_change)
    vline_thresholds[[paste(params$differential_fc_column, '>', log2(params$differential_min_fold_change))]] = log2(params$differential_min_fold_change)

    plot_args <- list(
      x = full_de[[params$differential_fc_column]],
      y = -log10(full_de[[pval_column]]),
      colorby = full_de$differential_status,
      ylab = paste("-log(10)", pval_column),
      xlab = xlabel <- paste("higher in", contrasts$reference[i], "          <<", params$differential_fc_column, ">>           higher in", contrasts$target[i]) ,
      labels = full_de[[label_col]],
      hline_thresholds = hline_thresholds,
      vline_thresholds = vline_thresholds,
      show_labels = FALSE,
      legend_title = "Differential status",
      palette = makeColorScale(2, 'Set1')
    )
  
    # Let's equalize the axes
    max_fc <- max(abs(full_de[[params$differential_fc_column]])) * 1.1
    p <- do.call(plotly_scatterplot, plot_args) %>%
      layout(xaxis = list(range=list(-max_fc, max_fc)))
 
    print(htmltools::tagList(p))
  }
  
  ## ... then show tables of the up/ down genes
  
  for (dir in c('up', 'down')){
    contrast_de <- sig_differential[[i]][[dir]]
    colnames(contrast_de) <- prettifyVariablename(colnames(contrast_de))
    
    if (nrow(contrast_de) > 0){
      print( htmltools::tagList(datatable(contrast_de, caption = paste('Differential genes', dir, 'in', contrast_descriptions[i], " (check", differential_files[[i]], "for more detail)"), rownames = FALSE) ))
    }else{
      cat(paste0("No signficantly differential '", dir, "' genes\n"))
    }
  }
}
```

<!-- Gene set analysis results -->

```{r, echo=FALSE, results='asis'}
possible_gene_set_methods <- c('gsea')
if (any(unlist(params[paste0(possible_gene_set_methods, '_run')]))){
  cat("\n### Gene set analysis\n")

  for (gene_set_method in possible_gene_set_methods){
    if (unlist(params[paste0(gene_set_method, '_run')])){
      cat("\n### ", toupper(gene_set_method) ," {.tabset}\n")
      
      reference_gsea_tables <- paste0(contrasts$id, '.gsea_report_for_', contrasts$reference, '.tsv')
      target_gsea_tables <- paste0(contrasts$id, '.gsea_report_for_', contrasts$target, '.tsv')

      for (i in 1:nrow(contrasts)){
        cat("\n#### ", contrast_descriptions[i], "\n")
        
        target_gsea_results <- read_metadata(target_gsea_tables[i])[,c(-2,-3)]
        print( htmltools::tagList(datatable(target_gsea_results, caption = paste0("\nTarget (", contrasts$target[i], ")\n"), rownames = FALSE) ))
        
        ref_gsea_results <- read_metadata(reference_gsea_tables[i])[,c(-2,-3)]
        print( htmltools::tagList(datatable(ref_gsea_results, caption = paste0("\nReference (", contrasts$reference[i], ")\n"), rownames = FALSE) ))
      }
    }
  }
}
```

# Methods

## Filtering

```{r, echo=FALSE, results='asis'}
make_params_table('feature-wise filtering', 'filtering_', remove_pattern = TRUE)
```

```{r, echo=FALSE, results='asis'}
filtering_string <- paste0('Filtering was carried out by selecting ', params$features_type, 's with an abundance of at least ', params$filtering_min_abundance)

if (is.null(params$filtering_grouping_var)){
  if (is.null(params$filtering_min_proportion)){
    filtering_string <- paste0(filtering_string, ' in at least ', params$filtering_min_samples, ' ', params$observations_type, 's.')
  }else{
    filtering_string <- paste0(filtering_string, ' in at least a proportion of ', params$filtering_min_proportion, ' of ', params$observations_type,'s.')
  }
}else{
  if (is.null(params$filtering_min_proportion)){
    filtering_string <- paste0(filtering_string, ' in at least the number of ', params$observations_type, 's corresponding to the smallest group size defined by the grouping variable "', params$filtering_grouping_var, '".')
  }else{
    filtering_string <- paste0(filtering_string, ' in at least a proportion of ', params$filtering_min_proportion, ' of the number of ', params$observations_type,'s corresponding to the smallest group size defined by the grouping variable"', params$filtering_grouping_var, '".')
  }
}
cat(filtering_string)
```

## Exploratory analysis

```{r, echo=FALSE, results='asis'}
make_params_table('exploratory analysis', 'exploratory_', remove_pattern = TRUE)
```

## Differential analysis

```{r, echo=FALSE, results='asis'}
if (params$study_type == 'rnaseq'){
  make_params_table('DESeq2', 'deseq2_', remove_pattern = TRUE)
}
make_params_table('downstream differential analysis', 'differential_', remove_pattern = TRUE)
```

<!-- If any gene set methods have been activated show their params -->

```{r, echo=FALSE, results='asis'}
possible_gene_set_methods <- c('gsea')

if (any(unlist(params[paste0(possible_gene_set_methods, '_run')]))){
  cat("\n### Gene set analysis\n")

  for (gene_set_method in possible_gene_set_methods){
    if (unlist(params[paste0(gene_set_method, '_run')])){
      cat("\n### ", toupper(gene_set_method) ,"  {.tabset}\n")
      make_params_table(toupper(gene_set_method), paste0(gene_set_method, '_'), remove_pattern = TRUE)
    }
  }

}
```

# Appendices

## All parameters

```{r, echo=FALSE, results='asis'}
print( htmltools::tagList(datatable(params_table, caption = "All parameters", rownames = FALSE) ))
```

## Software versions

**Note:** For a more detailed accounting of the software and commands used (including containers), consult the execution report produced as part of the 'pipeline info' for this workflow.

```{r, echo=FALSE, results='asis'}
versions_table <- data.frame(do.call(rbind, strsplit(names(versions), split = '\\.')), unlist(versions))
colnames(versions_table) <- c('Component', 'Software', 'Version')
print( htmltools::tagList(datatable(versions_table, caption = "Software versions", rownames = FALSE, options = list(dom = 'ft', paging = FALSE)) ))
```

```{r, echo=FALSE, results='asis'}
htmltools::includeMarkdown(params$citations)
```
