In zhuchcn/Metabase: Data container, handling, and visualization for high through-put quantitative experiment data.

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Introduction

Metabolomics are powerful experiment approaches that analyzes all, or a specific class, of small molecule compounds in a biological specimen. Lipidomics is a special case of Metabolomics that only focus on the lipids. Metabolomics and lipidomics are now being adopted many different type of studies with different purposes, as a tool to answer different scientific questions. Metabolomics and lipidomics often generates big data with feature numbers very from hundreds to thousands. The package Metabase aims to help researchers handle, analyze and visualize metabolomics and lipidomics data. This vignettes is a workflow of how import and process the lipidomics data reported by the West Coast Metabolomics Center, CA.

Install Metabase

The Metabase package can be installed from github

devtools::install_github("zhuchcn/Metabase")

Data

The data used in this vignette is a lipidomics experiment from a cross-over nutritional intervention study. 10 subjects were given 2 dietary treatment, and blood samples were taken before and after each treatment. The sample data comes in the package. If you would like to explore the sample data on your own, you can either copy them to your own directory, or download them using the link (right click and choose save link as):

[Download sample data] [Download standards]

Data import

Read data

Data import can be done using the function import_wcmc_excel_raw().

Arguments: + file: File path to the excel spreadsheet. + sheet: The sheet name with data. + conc_range: The cell range for concentration data. + sample_range: The cell range for sample data. + feature_range: The cell range for feature data. + InChIKey: The name of the column with InChI Key. The InChI Key is neccesary for later process and analysis.

library(Metabase)
file = file.path(system.file("extdata", package = "Metabase"), "CSH-QTOF_lipidomics.xlsx")
mset = import_wcmc_excel(
    file            = file, 
    sheet           = "Submit",
    conc_range      = "I10:BA613",
    sample_range    = "H1:BA9",
    feature_range   = "A9:H613",
    InChIKey        = "InChI Key",
    experiment_type = "Lipidomics"
)
mset

cat("Number of features:", nfeatures(mset),
    "\nNumber of samples: ", nsamples(mset))

Remove features

The dataset has 604 features. However 364 of them are unknow.

cat("Annotated:", sum(!is.na(feature_data(mset)$InChIKey)),
    "\nUnknown:  ", sum(is.na(feature_data(mset)$InChIKey)))

The unknown features can be removed using the subset_features function

mset = subset_features(mset, !is.na(feature_data(mset)$InChIKey))

Collapse QC samples

The first 5 samples are quality control samples.

head(sample_table(mset), 8)

To call the collapse_QC function, the mean, standard deviation, and coefficient of variance for each sample are be calculated and stored in the feature_data slot. And then the QC samples are be remove.

mset = collapse_QC(mset, qc_names = paste0("Biorec00", 1:5))
mset

Filling NAs

To see the missing values in the dataset, we can call the plot_hist_NA function. In this dataset, there are 3 features have 2 missing values, and 1 with 12.

plot_hist_NA(mset)

We can remove features with too many missing values using subset_features. The call below will keep all features that have less than 5 missing values.

mset = subset_features(
    mset, apply(conc_table(mset), 1, function(x) sum(is.na(x)) < 5) )

Then we can fill up the rest NAs using half of the lowest value of this feature in all samples.

mset = transform_by_feature(
    mset, function(x) ifelse(is.na(x), min(x, na.rm = TRUE)/2, x)
)

Calculate concentration using internal standards

The west coast metabolomics center's lipidomics assay spikes 15 complex lipid internal standards into each sample during analysis. Each of the 15 complex lipid internal standards represents a lipid class. To see the spike information, load the following csv file.

file = file.path(system.file("extdata", package = "Metabase"), "wcmc_lipidomics_standards.csv")
internal_standards = read.csv(file)
head(internal_standards)

Before the features can be calibrated to the internal standards, we need to first assign the lipid calss to each annotated feature. The function assign_lipid_class takes the annotation name and assign a lipid class to it. We also need to specify the ESI mode.

feature_data(mset)$class = assign_lipid_class(feature_data(mset)$Annotation)
feature_data(mset)$ESI = ifelse(grepl("\\+$", feature_data(mset)$Species),
                                "pos", "neg")

Next, the internal standard data can be addded to the experiment_data slot of the data object.

experiment_data(mset)$institute = "West Coast Metabolomics Center"
experiment_data(mset)$sample_volumn_ul = 20
experiment_data(mset)$internal_standards = internal_standards
experiment_data(mset)

Then the features can be calibrated.

mset = calibrate_lipidomics_wcmc(mset, cid = "InChIKey", 
                                 class = "class", ESI = "ESI")
mset

Filter by CV

The last problem is, some features are detected under both positive and negative ESI mode. This could be problomatic for example for some multi-variable analysis. We can only keep whichever with a lower CV.

mset = filter_by_cv(mset, cv = "qc_cv", cid = "InChIKey")
mset

The data now is cleaned up and is ready for any statistic analysis and visualization.

zhuchcn/Metabase documentation built on July 23, 2019, 1:36 p.m.