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Author: vsuksi

vignettes/Non-targeted_metabolomics_feature_prioritization.Rmd

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+---
+title: "Non-targeted metabolomics feature prioritization"
+author: "Anton Klåvus, Vilhelm Suksi"
+date: "`r Sys.Date()`"
+output:
+  BiocStyle::html_document:
+    toc: true
+    toc_depth: 2
+vignette: >
+  %\VignetteIndexEntry{Non-targeted metabolomics feature prioritization}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+bibliography: references.bib
+biblio-style: apalike
+---
+
+The statistics functionality in `notameStats` aims to identify interesting 
+features across study groups. See the project example vignette in the notame 
+package and [notame website reference index](https://hanhineva-lab.github.io/
+notame/reference/index.html) for listing of functions. Similar functionality is 
+available in several packages.
+
+Unless otherwise stated, all functions return separate data frames or other 
+objects with the results. These can be then added to the object feature data 
+using ```join_rowData(object, results)```. The reason for not adding these to 
+the objects automatically is that most of the functions return excess 
+information that is not always worth saving. We encourage you to choose which 
+information is important to you.
+
+```{r setup, include = FALSE}
+knitr::opts_chunk$set(
+  comment = "##",
+  message = FALSE
+)
+```
+
+# Installation
+
+To install `notameStats`, install `BiocManager` first, if it is not installed.
+Afterwards use the `install` function from `BiocManager` and load `notameStats`.
+
+```{r install, eval=FALSE}
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+BiocManager::install("notameStats")
+```
+
+```{r, message = FALSE}
+library(notame)
+library(notameViz)
+library(notameStats)
+
+ppath <- tempdir()
+init_log(log_file = file.path(ppath, "log.txt"))
+
+data(hilic_neg_sample, package = "notame")
+data(toy_notame_set, package = "notame")
+```
+
+# Univariate functions
+
+## Summary statistics and effect sizes
+
+It is straightforward to provide summary statistics and effect sizes for all 
+features:
+
+```{r}
+toy_notame_set <- mark_nas(toy_notame_set, value = 0)
+
+# Impute missing values, required especially for multivariate methods
+toy_notame_set <- notame::impute_rf(toy_notame_set)
+
+sum_stats <- summary_statistics(toy_notame_set, grouping_cols = "Group")
+toy_notame_set <- notame::join_rowData(toy_notame_set, sum_stats)
+
+d_results <- cohens_d(toy_notame_set, group = "Group")
+toy_notame_set <- notame::join_rowData(toy_notame_set, d_results)
+
+fc <- fold_change(toy_notame_set, group = "Group")
+toy_notame_set <- notame::join_rowData(toy_notame_set, fc)
+
+colnames(rowData(toy_notame_set))
+```