Qualitative meta-analysis results

In amanida: Meta-Analysis for Non-Integral Data

---

title: "Qualitative meta-analysis results" author: "Amanida R package" date: "r format(Sys.time(), '%d %B, %Y')" output: html_document: toc: true toc_float: true theme: lumen params: file_name: input_file separator: ";" analysis_type: "qual" column_id: coln votecount_lim: 1 comp_inf: FALSE show_code: FALSE

---

set.seed(123)
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(kableExtra, include.only = c('kbl', 'kable_styling', 'scroll_box', 'footnote'))
library(amanida)

Data exploration

Data is loaded using amanida_read function:

coln = c("Compound Name", "Behaviour", "References")
input_file <- system.file("extdata", "dataset2.csv", package = "amanida")
datafile <- amanida_read(input_file, mode = "qual", coln, separator=";")

Your data:

datafile <- amanida_read(params$file_name, params$analysis_type, params$column_id, params$separator) 

Here is shown the imported table:

data_imp <- datafile |> 
  mutate(trend = case_when(trend == -1 ~ "Down-regulated",
                           T ~ "Up-regulated")) |>
  rename("ID" = id,
         "Reference" = ref,
         "Behaviour" = trend)

kbl(data_imp, 
      booktabs = T, 
      caption = "Table 1: Data exploration",
    align = "c") |>
  kable_styling(latex_options = c("basic", "scale_down")) |>
  scroll_box(width = "100%", height = "400px")

Qualitative analysis

Qualitative analysis is performed by the vote-counting computed as follows: for compounds up-regulated are count as +1 and for down-regulated as -1. Total sum is obtained for each compound.

vote_results <- amanida_vote(datafile, comp.inf = params$comp_inf)

vote_results@vote

kbl(vote_results@vote |>
      arrange(desc(abs(votes))) |> 
      select(-c( "vote_counting")) |> 
      rename("ID" = id, "Votes" = votes), 
      booktabs = T, 
      caption = "Table 3: Qualitative analysis",
    align = "c") |>
  kable_styling(latex_options = c("basic", "scale_down")) |>
  scroll_box(width = "100%", height = "400px") 

Qualitative results can be also graphically inspected without trend division. Here the vote_plot is set to show the identifiers with a minimum of a vote-counting of r params$votecount_lim

vote_plot <- function(mets, counts = NULL) {

  votes = NULL; . = NULL;
  set.seed(123)

  col_palette <- amanida_palette()

  if (hasArg(counts)) { 
    cuts <- counts

    if (length(counts) != 1) {
      stop( "Please indicate one cut-off only")
    }
  } else {
    cuts <- 1
  }

  message("Cut-off for votes is ", cuts, ".", sep = "")

  # Subset vote-couting data
  tb <- as_tibble(mets@vote) |> 
    mutate(
      votes = as.numeric(votes),
      test = case_when(
      votes > 0 ~ 0,
      T ~ 1)) |>
    filter (abs(votes) >= cuts)

   if (nrow(tb) < 1) {
    stop( "Cut-off value out of limit")
  }

  if(nrow(tb) > 30) {
    message("Too much values, only showing 30 highest values. Please check counts parameter.")

   tb <- tb  |>
      slice_max(abs(votes), n = 30, with_ties = FALSE) 
  }

  max_p <- max(tb$votes)

      ggplot(tb, aes(reorder(id, votes), votes, fill = votes)) + 
        geom_bar(stat = "identity", show.legend = F, width = .5
        ) +
        geom_text(aes(label = reorder(id, votes)), vjust = 0.2, size = 3.5, 
                  hjust = tb$test) +
        scale_fill_gradient(low = col_palette[3], high = col_palette[5]) +
        theme_light() + 
        theme(axis.text.y = element_blank(),
              axis.text.x = element_text(size = 10),
              axis.title = element_text(size = 10),
              axis.ticks.y = element_blank(), 
              plot.title = element_text(size = 12), 
              panel.grid.minor = element_blank(),
              panel.grid.major.y = element_blank(), 
              panel.border = element_blank(), 
              panel.grid.major.x = element_line(linetype = "dashed"), 
              plot.tag = element_text(size = 9, colour = "grey"),
              plot.tag.position = "bottomright") +
        labs(tag = "Created with amanida") +
        coord_flip() +
        ylab("Vote-counting") +
        xlab('')+
        ggtitle("Total vote count of compounds behaviour") +
        scale_y_continuous(expand = c(0.6, 0), 
                           breaks = seq(max_p*-1, max_p, by = 1),
                           limits = c(max_p*-1,
                                      max_p + 1)
        )

}

vote_plot(vote_results, counts = params$votecount_lim)

Note: Plots are limited to 30 compounds for readability, for complete plot download from ubidi.shinyapps.io/easy-amanida

Here, the explore_plot will only display the relevant identifiers (elements bigger than 2 votes) or those with discrepancies between studies.

As dataset is big, on the following plot will be only displayed elements bigger than r params$votecount_lim votes and the elements with discrepancies between studies:

explore_plot(datafile, type = "mix", params$votecount_lim)

Note: Plots are limited to 30 compounds for readability, for complete plot download from ubidi.shinyapps.io/easy-amanida

Qualitative analysis using Amanida R package

amanida documentation built on March 30, 2022, 9:06 a.m.