knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = TRUE, out.width = "100%" )
In this version, we just added a new function output_eic()
. This can be used to generate peaks in some samples after you run process_data()
. For example, you analyzed data, and found some features are very important, so you want to check the peak shapes of them in QC samples, so you can use output_eic()
function.
First, you need to set the work directory to the folder which you used to run the process_data()
function. For example, in our example for process_Data
, we set the work directory in example/POS
, so here, we also set work directory in this folder.
output_eic()
Then we can run output_eic()
function.
library(massprocesser) output_eic(path = ".", query_sample_name = c("QC_1"), query_peak_name = c("M70T54_POS", "M70T579_POS"), polarity = "positive", threads = 4)
Then the peak shape of plots will be outputted in example/POS/Result
.
sessionInfo()
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