library(metaboprep)
# import data
dat <- read_metabolon(system.file("extdata", "metabolon_v1.1_example.xlsx", package = "metaboprep"), sheet="OrigScale")
# create the object
m <- Metaboprep(data = dat$data, samples = dat$samples, features = dat$features)
# run QC
m <- m |>
quality_control(source_layer = "input",
sample_missingness = 0.5,
feature_missingness = 0.3,
total_peak_area_sd = 5,
outlier_udist = 5,
outlier_treatment = "leave_be",
winsorize_quantile = 1.0,
tree_cut_height = 0.5,
pc_outlier_sd = 5,
sample_ids = NULL,
feature_ids = NULL)
#>
#> ── Starting Metabolite QC Process ──────────────────────────────────────────────
#> ℹ Validating input parameters
#> ✔ Validating input parameters [8ms]
#>
#> ℹ Sample & Feature Summary Statistics for raw data
#> ✔ Sample & Feature Summary Statistics for raw data [866ms]
#>
#> ℹ Copying input data to new 'qc' data layer
#> ✔ Copying input data to new 'qc' data layer [30ms]
#>
#> ℹ Assessing for extreme sample missingness >=80% - excluding 0 sample(s)
#> ✔ Assessing for extreme sample missingness >=80% - excluding 0 sample(s) [16ms]
#>
#> ℹ Assessing for extreme feature missingness >=80% - excluding 0 feature(s)
#> ✔ Assessing for extreme feature missingness >=80% - excluding 0 feature(s) [17m…
#>
#> ℹ Assessing for sample missingness at specified level of >=50% - excluding 0 sa…
#> ✔ Assessing for sample missingness at specified level of >=50% - excluding 0 sa…
#>
#> ℹ Assessing for feature missingness at specified level of >=30% - excluding 0 f…
#> ✔ Assessing for feature missingness at specified level of >=30% - excluding 0 f…
#>
#> ℹ Calculating total peak abundance outliers at +/- 5 Sdev - excluding 0 sample(…
#> ✔ Calculating total peak abundance outliers at +/- 5 Sdev - excluding 0 sample(…
#>
#> ℹ Running sample data PCA outlier analysis at +/- 5 Sdev
#> ✔ Running sample data PCA outlier analysis at +/- 5 Sdev [16ms]
#>
#> ℹ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#> [1] 2
#> [1] 100 10
#> ✔ Sample PCA outlier analysis - re-identify feature independence and PC outlier…
#>
#> ℹ Creating final QC dataset...
#> ✔ Creating final QC dataset... [849ms]
#>
#> ℹ Metabolite QC Process Completed
#> ✔ Metabolite QC Process Completed [27ms]
#>
# render report
generate_report(m,
project = "myproject",
output_dir = getwd(),
output_filename = NULL,
format = "html",
template = "qc_report")
#> processing file: skeleton.Rmd
#> output file: skeleton.knit.md
#> /opt/hostedtoolcache/pandoc/3.1.11/x64/pandoc +RTS -K512m -RTS skeleton.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /home/runner/work/metaboprep/metaboprep/vignettes/myproject_metaboprep_qc_report.html --lua-filter /home/runner/work/_temp/Library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /home/runner/work/_temp/Library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 2 --template /home/runner/work/_temp/Library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --number-sections --variable theme=bootstrap --css styles.css --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmpDVyuB5/rmarkdown-str226b1201fbd4.html
#>
#> Output created: myproject_metaboprep_qc_report.html
#> [1] TRUE
