From d38ec3b247fe030e10868cda017f01735a7db539 Mon Sep 17 00:00:00 2001 From: Johannes Rainer Date: Thu, 3 Oct 2024 08:51:57 +0200 Subject: [PATCH] Update MxP500 documentation --- inst/rmds/using-chris-mxp500-tdff.Rmd | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/inst/rmds/using-chris-mxp500-tdff.Rmd b/inst/rmds/using-chris-mxp500-tdff.Rmd index 65d8c45..9b8cc6f 100644 --- a/inst/rmds/using-chris-mxp500-tdff.Rmd +++ b/inst/rmds/using-chris-mxp500-tdff.Rmd @@ -18,8 +18,9 @@ BiocStyle::markdown() # Introduction This document describes how the Biocrates MxP500-based targeted metabolomics -data from the CHRIS study [@pattaro_cooperative_2015] -[@verri_hernandes_age_2022] can be loaded and analyzed in R. +data from the CHRIS study [@pattaro_cooperative_2015] can be loaded and analyzed +in R. This data set provides metabolite and lipid abundances from a subset of +CHRIS participants. The provided *values* for each metabolite are absolute concentrations in **natural scale**. @@ -136,9 +137,7 @@ quantile(metabo_data$Gly, na.rm = TRUE) As we can see from the values above they are in **natural scale** - so, for data analysis it might be better to transform them using `log2` or `log10` (which -will also ensure the data to be more Gaussian distributed). See also -[@verri_hernandes_age_2022] for more information on data distribution and -quality. +will also ensure the data to be more Gaussian distributed). At last we also evaluate the distribution of the coefficients of variation for all analytes in the data set.