vignettes/version_0_9_2.Rmd
version_0_9_2.RmdIn 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(masscleaner)
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()
#> R Under development (unstable) (2022-01-11 r81473)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur/Monterey 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> loaded via a namespace (and not attached):
#> [1] rprojroot_2.0.2 digest_0.6.29 crayon_1.4.2 R6_2.5.1
#> [5] jsonlite_1.7.2 magrittr_2.0.1 evaluate_0.14 stringi_1.7.6
#> [9] rlang_0.4.12 cachem_1.0.6 fs_1.5.2 jquerylib_0.1.4
#> [13] bslib_0.3.1 ragg_1.2.1 rmarkdown_2.11 pkgdown_2.0.1
#> [17] textshaping_0.3.6 desc_1.4.0 tools_4.2.0 stringr_1.4.0
#> [21] purrr_0.3.4 yaml_2.2.1 xfun_0.29 fastmap_1.1.0
#> [25] compiler_4.2.0 systemfonts_1.0.3 memoise_2.0.1 htmltools_0.5.2
#> [29] knitr_1.37 sass_0.4.0