README.md

In amanida: Meta-Analysis for Non-Integral Data

amanida: a R package for meta-analysis with non-integral data

Description

Amanida package contains a collection of functions for computing a meta-analysis in R only using significance and effect size. It covers the lack of data provided on metabolomic studies, where is rare to have error or variance disclosed. With this adaptation, only using p-value and fold-change, global significance and effect size for compounds or metabolites are obtained.

Furthermore, Amanida also computes qualitative meta-analysis performing a vote-counting for compounds, including the option of only using identifier and trend labels.

Documentation

The following computations are included:

• P-value combination: Fisher's method weighted by number of participants on the study.
• Fold-change combination: logarithmic transformation for average with weighting by number of participants.
• Compound vote-counting: votes are +1 for up-regulation, -1 for down-regulation and 0 if no trend. The total votes are divided by the number of reports.

The following plots are included to visualize the results:

• Volcano plot of meta-analysis results: showing compounds labels for over the selected cut-off.
• Bar plot of qualitative results.
• Bar plot of reports divided by trend including the total vote-counting.

Installation

Beta/Github release:

Installation using R package devtools:

install.packages("devtools")
devtools::install_github("mariallr/amanida")

CRAN:

install.packages("amanida")

Usage

You can use Amanida package in RStudio or R. After installation (explained before) follow this steps:

1. Load package in your script:

library(amanida)

2. Read your data: amanida_read

Supported files are csv, xls/xlsx and txt.

For quantitative meta-analysis include the following parameters:

• Indicate mode = "quan"
• coln: vector containing the column names, which need to be in this order:
• Id: compound name or unique identification
• P-value
• Fold-change
• N: number of individuals in the study
• Reference: bibliographic reference of the results

coln = c("Compound Name", "P-value", "Fold-change", "N total", "References")
input_file <- system.file("extdata", "dataset2.csv", package = "amanida")
datafile <- amanida_read(input_file, mode = "quan", coln, separator=";")

For qualitative meta-analysis include the following parameters:

• Indicate mode = "qual"
• coln: vector containing the column names, which need to be in this order:
• Id: compound name or unique identification
• Trend: can be up-regulated or down-regulated
• Reference: bibliographic reference of the results

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

3. Perform adapted meta-analysis: compute_amanida

amanida_result <- compute_amanida(datafile)

In this step you will obtain an S4 object with two tables:

• adapted meta-analysis acces by amanida_result@stat
• vote-counting acces by amanida_results@vote

4. Perform quanlitative meta-analysis: amanida_vote

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

vote_result <- amanida_vote(data_votes)

In this step you will obtain an S4 object with one table:

• vote-counting acces by vote_results@vote

Plots

Graphical visualization for adapted meta-analysis results: volcano_plot

volcano_plot(amanida_result, cutoff = c(0.05,4))

Graphical visualization of compounds vote-counting: vote_plot

Data can be subset for better visualization using counts parameter to indicate the vote-counting cut-off.

vote_plot(amanida_result)

Graphical visualization of compounds vote-counting and reports divided trend: explore_plot

Data can be shown in three types: type = "all": show all data type = "sub": subset the data by a cut-off value indicated by the counts parameter * type = "mix": subset the data by a cut-off value indicated by the counts parameter and show compounds with discrepancies (reports up-regulated and down-regulated)

explore_plot(sample_data, type = "mix", counts = 1)

Report

All results using Amanida can be obtained in a single step using amanida_report function. It only requires the following parameters for qualitative analysis report: file: path to the dataset separator: separator used in the dataset analysis_type: specify "quan" column_id: nomes of columns to be used, see amanida_read documentation for more information pvalue_cutoff: numeric value where the p-value will be considered as significant, usually 0.05 fc_cutoff: numeric value where the fold-change will be considered as significant, usually 2 * votecount_lim: numeric value set as minimum to show vote-counting results

And for quantitative analysis report: file: path to the dataset separator: separator used in the dataset analysis_type: specify "qual" column_id: nomes of columns to be used, see amanida_read documentation for more information * votecount_lim: numeric value set as minimum to show vote-counting results

column_id = c("Compound Name", "P-value", "Fold-change", "N total", "References")
input_file <- system.file("extdata", "dataset2.csv", package = "amanida")
amanida_report(input_file, 
                separator = ";", 
                column_id, 
                analysis_type = "quan", 
                pvalue_cutoff = 0.05, 
                fc_cutoff = 4, 
                votecount_lim = 2)

Examples

There is an example dataset installed, to run examples please load:

data("sample_data")

The dataset consist in a short list of compounds extracted from Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis Mallafré et al. Cancers 2021, 13(11), 2534; https://doi.org/10.3390/cancers13112534

Please fill an issue if you have any question or problem :)

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