Split an untargeted metabolomics data set into a set of likely true metabolites and a set of likely measurement artifacts. This process involves comparing missing rates of pooled plasma samples and biological samples. The functions assume a fixed injection order of samples where biological samples are randomized and processed between intermittent pooled plasma samples. By comparing patterns of missing data across injection order, metabolites that appear in blocks and are likely artifacts can be separated from metabolites that seem to have random dispersion of missing data. The two main metrics used are: 1. the number of consecutive blocks of samples with present data and 2. the correlation of missing rates between biological samples and flanking pooled plasma samples.
|Date of publication||2016-05-19 05:46:39|
|Maintainer||Mark Chaffin <email@example.com>|
|License||GPL (>= 2)|
corr_metric: Calculate Correlation of Missing Rates between Pooled Plasma...
get_group: Retrieve Index of Biological Samples and Pooled Plasma...
get_missing: Compute Missing Rates of Biological Samples and Pooled Plasma...
heatmap_res: Plot Patterns of Missing Data Across Metabolites
met_proc: Separates Metabolites into Likely True Metabolites and Likely...
MetProc-package: Separate Untargeted Metabolites into Likely Artifacts and...
plot_metric: Plot Distribution of Metabolite Quality Metrics for Each...
plot_pp_sample_missing: Plot Pooled Plasma and Biological Sample Missing Rates
read.met: Read in a Metabolomics Dataset of Standard Structure
run_metric: Calculate Longest Run of Blocks where Data is Present
sampledata: Simulated Metabolomics Data
subset_met: Group Metabolites based on Pooled Plasma Missing Rate
write.met: Write Metabolomics Dataset of Standard Structure
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