In tidymass/tinytools: All the usefull tools for MS data processing and analysis

knitr::opts_chunk$set(echo = TRUE)

**Analysis report**

Xiaotao Shen, Zhengjiang Zhu

`r format(Sys.Date())`

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(1) INTRODUCTION

MetDNA (version 0.99.07) is used for metabolite annotation and dysregulated network analysis of untargeted metabolomics.

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(2) PARAMETERS

Table 1: The parameter setting of this analysis

library(knitr)
load("parameter")
colnames(parameter) <- c("Patameter", "Value", "Meaning")
kable(parameter, format = "markdown")

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(3) SAMPLE INFORMATION

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(4) METABOLITE ANNOTATION

The metabolite annotation is based on metabolic reaction network.

Confidence level (from grade 1 to grad 4) is assigned to each metabolie.

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(5) DYSREGULATED NETWORK ANALYSIS

Dysregulated peaks (according to pvalues) are used to identify dysregulated modules. The dysregulated modules with p values less than 0.05 are combined as dysregulated network. Metabolite set analysis (MSEA) is used to annotate functions of each module. The MSEA result for each module can be found in /Dysregulated_network_analysis_result_POS/module_information/Module_MSE analysis.

Table 2: The information of dysregualted modules. The detailed information can be got from module.result.csv in /Dysregulated_network_analysis_result_POS/module_information

library(knitr)
load("module.result")
# temp.idx <- which(module.result$p.value < 0.05)
# if(length(temp.idx) == 0 & nrow(module.result) > 5){
#   temp.idx <- 1:5
# }else{
#   temp.idx <- temp.idx
# }

overlap <- as.numeric(module.result$`Detected metabolite number`)*100/as.numeric(module.result$`Module size`)
module.result$`Detected metabolite number` <- overlap
colnames(module.result)[5] <- "Overlap (%)"
kable(module.result[,c(1,2,3,4,5,13)])

There are no modules with p values less than 0.05, so no dysregulated network is identified.

tidymass/tinytools documentation built on Jan. 2, 2022, 5:18 p.m.