+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+MotrpacBicQC: Metabolomics QC
+ +2020-09-21
+ + Source:vignettes/qc_metabolomics.Rmd
+ qc_metabolomics.Rmd
+
+
+
++Installation
+First, download and install R and RStudio:
+ +Then, open RStudio and install the devtools package
install.packages("devtools")
Finally, install the MotrpacBicQC package
library(devtools) +devtools::install_github("MoTrPAC/MotrpacBicQC", build_vignettes = TRUE)
+
+
+
+
++Usage
+Load the library
+library(MotrpacBicQC)
And run any of the following tests to check that the package is correctly installed and it works. For example:
+# Just copy and paste in the RStudio terminal + +check_metadata_metabolites(df = metadata_metabolites_named, name_id = "named") +check_metadata_samples(df = metadata_sample_named, cas = "umichigan") +check_results(r_m = results_named, m_s = metadata_sample_named, m_m = metadata_metabolites_named)
which should generate the following output:
+check_metadata_metabolites(df = metadata_metabolites_named, name_id = "named") +#> + (+) All required columns present +#> + (+) {metabolite_name} OK +#> + (+) {refmet_name} unique values: OK +#> + (+) {refmet_name} ids found in refmet: OK +#> + (+) {rt} all numeric: OK +#> + (+) {mz} all numeric: OK +#> + (+) {neutral_mass} all numeric values OK +#> + (+) {formula} available: OK +check_metadata_samples(df = metadata_sample_named, cas = "umichigan") +#> + (+) {sample_id} seems OK +#> + (+) {sample_type} seems OK +#> + (+) {sample_order} is numeric +#> + (+) {sample_order} unique values OK +#> + (+) {raw_file} unique values OK +check_results(r_m = results_named, m_s = metadata_sample_named, m_m = metadata_metabolites_named) +#> + (+) All samples from [results_metabolite] are available in [metadata_sample] +#> + (+) {metabolite_name} is identical in both [results] and [metadata_metabolites] files: OK +#> + (+) {sample_id} columns are numeric: OK
+
++How to test your datasets
+Two approaches available:
+
+
+Check full
+
+
+
+Check full PROCESSED_YYYYMMDD folder (recommended)
+Run test on the full submission. For that, run the following command:
+validate_metabolomics(input_results_folder = "/full/path/to/PROCESSED_YYYYMMDD", + cas = "your_site_code")
cas is one of the followings:
+-
+
- “broad_met” = Broad Metabolomics +
- “emory” = Emory +
- “mayo” = Mayo Clinic +
- “umichigan” = Umichigan +
- “gtech” = Georgia Tech +
- “duke” = Duke +
+
+
+
+
++Check individual files
+-
+
- Check metadata metabolites: +
# Open the metadata_metabolites file(s) + +metadata_metabolites_named <- read.delim(file = "/path/to/your/file", stringsAsFactors = FALSE) +metadata_metabolites_unnamed <- read.delim(file = "/path/to/your/file", stringsAsFactors = FALSE) + +check_metadata_metabolites(df = metadata_metabolites_named, name_id = "named") +check_metadata_metabolites(df = metadata_metabolites_unnamed, name_id = "unnamed")
-
+
- Check metadata samples: +
# Open your files +metadata_sample_named <- read.delim(file = "/path/to/your/file", stringsAsFactors = FALSE) +metadata_sample_unnamed <- read.delim(file = "/path/to/your/file", stringsAsFactors = FALSE) + +check_metadata_samples(df = metadata_sample_named, cas = "your_side_id") +check_metadata_samples(df = metadata_sample_unnamed, cas = "your_side_id")
-
+
- Check results, which needs both both metadata metabolites and samples +
# Open your files +metadata_metabolites_named <- read.delim(file = "/path/to/your/file", stringsAsFactors = FALSE) +metadata_sample_named <- read.delim(file = "/path/to/your/file", stringsAsFactors = FALSE) +results_named <- read.delim(file = "/path/to/your/file", stringsAsFactors = FALSE) + +check_results(r_m = results_named, + m_s = metadata_sample_named, + m_m = metadata_metabolites_named)
+
+
+
++Merge metabolomics data (only PASS1A supported)
+The following functions enable merging all results and metadata files into a single data frame.
+The folder/file structure of a required untargeted metabolomics submission is as follows:
+PASS1A-06/
+ T55/
+ HILICPOS/
+ BATCH1_20190725/
+ RAW/
+ Manifest.txt
+ file1.raw
+ file2.raw
+ etc
+ PROCESSED_20190725/
+ metadata_failedsamples_[cas_specific_labeling]. txt
+ NAMED/
+ results_metabolites_named_[cas_specific_labeling].txt
+ metadata_metabolites_named_[cas_specific_labeling].txt
+ metadata_sample_named_[cas_specific_labeling].txt
+ metadata_experimentalDetails_named_[cas_specific_labeling].txt
+ UNNAMED/
+ results_metabolites_unnamed_[cas_specific_labeling].txt
+ metadata_metabolites_unnamed_[cas_specific_labeling].txt
+ metadata_sample_unnamed_[cas_specific_labeling].txt
+ metadata_experimentalDetails_unnamed_[cas_specific_labeling].txtWith the following file relations…
+
To merge all data available in a PROCESSED_YYYYMMDD folder, run the following command:
t31_ionpneg <- combine_metabolomics_batch(input_results_folder = "/full/path/to/PROCESSED_YYYYMMDD/", + cas = "umichigan")
Alternatively, each individual dataset can also be provided. For example:
+plasma.untargeted.merged <- + merge_all_metabolomics(m_m_n = metadata_metabolites_named, + m_m_u = metadata_metabolites_unnamed, + m_s_n = metadata_sample_named, + r_n = results_named, + r_u = results_unnamed, + phase = "PASS1A-06")
Check the function help for details
+
+
+
+
+ +Help
+Additional details for each function can be found by typing, for example:
+?merge_all_metabolomicsNeed extra help? Please, submit an issue here providing as many details as possible.
+