For a given number of splits of data based on pooled plasma missing rate, calculate the longest run metric (`run_metric`

) and the correlation metric (`corr_metric`

) for metabolites in each group. Plot the distribution of these metrics for each group color coding those that exceed thresholds.

1 2 3 |

`df` |
The metabolomics dataset, ideally read from the |

`ppkey` |
The unique prefix of pooled plasma samples. Default is |

`sidkey` |
The unique prefix of biological samples. Default is |

`numsplit` |
The number of equal sized sections to divide metabolites into based on missing rate of pooled plasma columns. Divides the range of missing rates between |

`mincut` |
A cutoff to specify that any metabolite with pooled plasma missing rate less than or equal to this value should be retained. Default is |

`maxcut` |
A cutoff to specify that any metabolite with pooled plasma missing rate greater than this value should be removed. Default is |

`scut` |
The cutoff of missingness to consider a metabolite as having data present in a given biological sample block. Relevant only to |

`cor_rates` |
A vector of length equal to |

`runlengths` |
A vector of length equal to |

`histcolors` |
A vector of length equal to |

Returns histograms showing the correlation metric and longest run metric distributions for each group of the metabolites based on pooled plasma missing rate.

See `MetProc-package`

for examples of running the full process.

1 2 3 4 5 6 7 8 9 10 | ```
library(MetProc)
#Read in metabolomics data
metdata <- read.met(system.file("extdata/sampledata.csv", package="MetProc"),
headrow=3, metidcol=1, fvalue=8, sep=",", ppkey="PPP", ippkey="BPP")
#Plot distributions of the two metrics for each group
plot_metric(metdata,ppkey='PPP',sidkey='X',numsplit=5,mincut=0.02,maxcut=0.95,
scut=0.5,cor_rates=c(.6,.65,.65,.65,.6),runlengths=c(NA,15,15,15,NA),
histcolors=c('red','yellow','green','blue','purple'))
``` |

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