diff --git a/DESCRIPTION b/DESCRIPTION index b628768..643caac 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: metaboMisc Title: Miscellaneous Functions for Metabolomics Analyses -Version: 0.5.7 +Version: 0.5.8 Authors@R: person("Jasen", "Finch", email = "jsf9@aber.ac.uk", role = c("aut", "cre")) Description: Miscellaneous helper functions for metabolomics analyses that do not yet have a permanent home. URL: https://jasenfinch.github.io/metaboMisc diff --git a/NAMESPACE b/NAMESPACE index 637c14e..0804971 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -83,6 +83,7 @@ importFrom(profilePro,processingParameters) importFrom(profilePro,technique) importFrom(purrr,map) importFrom(purrr,map_chr) +importFrom(purrr,map_df) importFrom(readr,write_csv) importFrom(stats,IQR) importFrom(stats,quantile) diff --git a/NEWS.md b/NEWS.md index a4098f8..afb4bb1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +# metabMisc 0.5.8 + +* Numbers of characters in strings are now limited by `sanitiseTable()`. + # metaboMisc 0.5.7 * Removed `aberHRML/metaboData` from the Remotes field in the DESCRIPTION to ensure that the CRAN version of metaboData is installed. diff --git a/R/sanitise.R b/R/sanitise.R index 1351a1d..dda734f 100644 --- a/R/sanitise.R +++ b/R/sanitise.R @@ -1,15 +1,17 @@ #' Sanitise a data table -#' @description Sanitise a data table by restricting the number of rows and rounding numeric columns. +#' @description Sanitise a data table by restricting the number of rows or characters and rounding numeric columns. #' @param x A tibble or data.frame containing the data to be sanitised #' @param maxRows Maximum number of rows with which to restrict the table #' @param sigFig Significant figures with which to round numeric columns +#' @param maxCharacters Maximum number of characters allowed in a string before it is truncated #' @examples #' sanitiseTable(iris,maxRows = 10,sigFig = 1) #' @importFrom dplyr mutate_if +#' @importFrom purrr map_df #' @export -sanitiseTable <- function(x,maxRows = 5000,sigFig = 3){ +sanitiseTable <- function(x,maxRows = 5000,sigFig = 3,maxCharacters = 100){ x <- mutate_if(x,is.numeric,signif,digits = sigFig) if (nrow(x) > maxRows){ @@ -17,5 +19,18 @@ sanitiseTable <- function(x,maxRows = 5000,sigFig = 3){ x <- x[seq_len(maxRows),] } + a <- map_df(x,~{ + if (typeof(.x) == 'character'){ + limit_characters <- .x %>% + nchar() %>% + {. > maxCharacters} + + .x[limit_characters] <- str_sub(.x[limit_characters],1,maxCharacters) %>% + str_c(.,'...') + } + + return(.x) + }) + return(x) } diff --git a/_pkgdown.yml b/_pkgdown.yml index 146a9c3..3ed8d62 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1 +1 @@ -url: https://jasenfinch.github.io/metaboMisc/ \ No newline at end of file +url: https://jasenfinch.github.io/metaboMisc/ diff --git a/docs/404.html b/docs/404.html index e24e55f..688cbb6 100644 --- a/docs/404.html +++ b/docs/404.html @@ -71,7 +71,7 @@ metaboMisc - 0.5.7 + 0.5.8 diff --git a/docs/authors.html b/docs/authors.html index d46507d..e766a83 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -71,7 +71,7 @@ metaboMisc - 0.5.7 + 0.5.8 diff --git a/docs/index.html b/docs/index.html index f85b484..24b1da3 100644 --- a/docs/index.html +++ b/docs/index.html @@ -31,7 +31,7 @@ metaboMisc - 0.5.7 + 0.5.8 diff --git a/docs/news/index.html b/docs/news/index.html index 235ee42..ac75df0 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -71,7 +71,7 @@ metaboMisc - 0.5.7 + 0.5.8 @@ -114,6 +114,13 @@

Changelog

Source: NEWS.md +
+

+metabMisc 0.5.8

+ +

metaboMisc 0.5.7

@@ -176,7 +183,7 @@

  • Improved documentation and added examples.

  • Added unit testing infrastructure.

  • Added detectPretreatmentParameters and detectModellingParameters methods.

    • -

  • The magrittr pipe (%>%) is now re-exported.

    • +

  • The magrittr pipe (%>%) is now re-exported.

    • diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 6e16535..696fe44 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 2.11.4 pkgdown: 1.6.1 pkgdown_sha: ~ articles: {} -last_built: 2021-09-27T10:19Z +last_built: 2021-11-17T09:52Z urls: reference: https://jasenfinch.github.io/metaboMisc//reference article: https://jasenfinch.github.io/metaboMisc//articles diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png index 70b7b59..a0035e2 100644 Binary files a/docs/reference/Rplot002.png and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/addAssignments.html b/docs/reference/addAssignments.html index 2898bbb..72ba628 100644 --- a/docs/reference/addAssignments.html +++ b/docs/reference/addAssignments.html @@ -72,7 +72,7 @@ metaboMisc - 0.5.7 + 0.5.8
    diff --git a/docs/reference/convertSampleInfo.html b/docs/reference/convertSampleInfo.html index 344f27f..b6fb723 100644 --- a/docs/reference/convertSampleInfo.html +++ b/docs/reference/convertSampleInfo.html @@ -72,7 +72,7 @@ metaboMisc - 0.5.7 + 0.5.8 diff --git a/docs/reference/detectBatchDiff.html b/docs/reference/detectBatchDiff.html index d892d75..548b349 100644 --- a/docs/reference/detectBatchDiff.html +++ b/docs/reference/detectBatchDiff.html @@ -72,7 +72,7 @@ metaboMisc - 0.5.7 + 0.5.8 @@ -153,25 +153,44 @@

    Details

    Analysis of Variance (ANOVA) is used to detect differences in total ion count (TIC) averages between batches/blocks.

    Examples

    - 
    ## Retrieve file paths and sample information for example data
-files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
-
-info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
-
-## Perform spectral binning
-analysis <- binneR::binneRlyse(files, 
-                               info, 
-                               parameters = binneR::detectParameters(files))
-
    #> 
-#> Attaching package: ‘purrr’
    #> The following object is masked from ‘package:metaboMisc’:
-#> 
-#>     reduce
    #> binneR v2.5.3 Mon Sep 27 11:19:18 2021
    #> ________________________________________________________________________________
    #> Scans: 5:14 
    #> ________________________________________________________________________________
    #> Reading raw data
    #> Gathering bins
    #> Removing single scan events
    #> Averaging intensities across scans
    #> Calculating bin metrics
    #> Calculating accurate m/z
    #> Building intensity matrix
    #> Gathering file headers
    #> 
    #> Completed! [2.9S]
    
-## Detect batch differences
-batch_diff <- detectBatchDiff(analysis)
-
    #> Batches with < 3 replicates removed: "3", "5"
    #> Only 1 batch detected, skipping detection
    
-## Display batch diffferences
-batch_diff
-
    #> NULL
    + 
    ## Retrieve file paths and sample information for example data
+files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
+
+info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
+
+## Perform spectral binning
+analysis <- binneR::binneRlyse(files, 
+                               info, 
+                               parameters = binneR::detectParameters(files))
+#> 
+#> Attaching package: ‘purrr’
+#> The following object is masked from ‘package:metaboMisc’:
+#> 
+#>     reduce
+#> binneR v2.6.2 Wed Nov 17 09:52:25 2021
+#> ________________________________________________________________________________
+#> Scans: 5:14 
+#> ________________________________________________________________________________
+#> Reading raw data
+#> Gathering bins
+#> Removing single scan events
+#> Averaging intensities across scans
+#> Calculating bin metrics
+#> Calculating accurate m/z
+#> Building intensity matrix
+#> Gathering file headers
+#> 
+#> Completed! [2.6S]
+
+## Detect batch differences
+batch_diff <- detectBatchDiff(analysis)
+#> Batches with < 3 replicates removed: "3", "5"
+#> Only 1 batch detected, skipping detection
+
+## Display batch diffferences
+batch_diff
+#> NULL
+
    @@ -149,22 +149,37 @@

    Details

    Samples with a total ion count (TIC) below 1.5 times the inter-quartile range are detected as miss injections.

    Examples

    - 
    ## Retrieve file paths and sample information for example data
-files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
-
-info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
-
-## Perform spectral binning
-analysis <- binneR::binneRlyse(files, 
-                               info, 
-                               parameters = binneR::detectParameters(files))
-
    #> binneR v2.5.3 Mon Sep 27 11:19:22 2021
    #> ________________________________________________________________________________
    #> Scans: 5:14 
    #> ________________________________________________________________________________
    #> Reading raw data
    #> Gathering bins
    #> Removing single scan events
    #> Averaging intensities across scans
    #> Calculating bin metrics
    #> Calculating accurate m/z
    #> Building intensity matrix
    #> Gathering file headers
    #> 
    #> Completed! [2.3S]
    
-## Detect miss injections
-miss_injections <- detectMissInjections(analysis)
-
-## Display detected miss injections
-miss_injections$missInjections
-
    #> numeric(0)
    + 
    ## Retrieve file paths and sample information for example data
+files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
+
+info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
+
+## Perform spectral binning
+analysis <- binneR::binneRlyse(files, 
+                               info, 
+                               parameters = binneR::detectParameters(files))
+#> binneR v2.6.2 Wed Nov 17 09:52:29 2021
+#> ________________________________________________________________________________
+#> Scans: 5:14 
+#> ________________________________________________________________________________
+#> Reading raw data
+#> Gathering bins
+#> Removing single scan events
+#> Averaging intensities across scans
+#> Calculating bin metrics
+#> Calculating accurate m/z
+#> Building intensity matrix
+#> Gathering file headers
+#> 
+#> Completed! [1.9S]
+
+## Detect miss injections
+miss_injections <- detectMissInjections(analysis)
+
+## Display detected miss injections
+miss_injections$missInjections
+#> numeric(0)
+
    @@ -157,22 +157,37 @@

    Value

    S4 object of class AnalysisParameters

    Examples

    - 
    ## Retrieve file paths and sample information for example data
-files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
-
-info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
-
-## Perform spectral binning
-analysis <- binneR::binneRlyse(files, 
-                               info, 
-                               parameters = binneR::detectParameters(files))
-
    #> binneR v2.5.3 Mon Sep 27 11:19:25 2021
    #> ________________________________________________________________________________
    #> Scans: 5:14 
    #> ________________________________________________________________________________
    #> Reading raw data
    #> Gathering bins
    #> Removing single scan events
    #> Averaging intensities across scans
    #> Calculating bin metrics
    #> Calculating accurate m/z
    #> Building intensity matrix
    #> Gathering file headers
    #> 
    #> Completed! [2.2S]
    
-## Detect modelling parameters
-modelling_parameters <- detectModellingParameters(analysis)
-
-modelling_parameters
-
    #> Parameters:
-#>
    + 
    ## Retrieve file paths and sample information for example data
+files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
+
+info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
+
+## Perform spectral binning
+analysis <- binneR::binneRlyse(files, 
+                               info, 
+                               parameters = binneR::detectParameters(files))
+#> binneR v2.6.2 Wed Nov 17 09:52:32 2021
+#> ________________________________________________________________________________
+#> Scans: 5:14 
+#> ________________________________________________________________________________
+#> Reading raw data
+#> Gathering bins
+#> Removing single scan events
+#> Averaging intensities across scans
+#> Calculating bin metrics
+#> Calculating accurate m/z
+#> Building intensity matrix
+#> Gathering file headers
+#> 
+#> Completed! [1.9S]
+
+## Detect modelling parameters
+modelling_parameters <- detectModellingParameters(analysis)
+
+modelling_parameters
+#> Parameters:
+#> 
+
    @@ -142,55 +142,71 @@

    Value

    S4 object of class AnalysisParameters

    Examples

    - 
    ## Retreive example file paths and sample information 
-file_paths <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes') %>% 
-   .[61:63]
-
-sample_information <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes') %>% 
-    dplyr::filter(name == 'QC01' | name == 'QC02' | name == 'QC03')
-
-## Detect spectral binning parameters
-bp <- binneR::detectParameters(file_paths)
-
-## Perform spectral binning
-bd <- binneR::binneRlyse(file_paths,sample_information,bp)
-
    #> binneR v2.5.3 Mon Sep 27 11:19:28 2021
    #> ________________________________________________________________________________
    #> Scans: 5:13 
    #> ________________________________________________________________________________
    #> Reading raw data
    #> Gathering bins
    #> Removing single scan events
    #> Averaging intensities across scans
    #> Calculating bin metrics
    #> Calculating accurate m/z
    #> Building intensity matrix
    #> Gathering file headers
    #> 
    #> Completed! [3.2S]
    
-## Detect pre-treatment parameters
-pp <- detectPretreatmentParameters(bd) 
-
    #> Only 1 batch detected, skipping detection
    
-pp
-
    #> Parameters:
-#> pre-treatment
-#> 	QC
-#> 		occupancyFilter
-#> 			cls = class
-#> 			QCidx = QC
-#> 			occupancy = 2/3
-#> 		impute
-#> 			cls = class
-#> 			QCidx = QC
-#> 			occupancy = 2/3
-#> 			parallel = variables
-#> 			seed = 1234
-#> 		RSDfilter
-#> 			cls = class
-#> 			QCidx = QC
-#> 			RSDthresh = 50
-#> 		removeQC
-#> 			cls = class
-#> 			QCidx = QC
-#> 	occupancyFilter
-#> 		maximum
-#> 			cls = class
-#> 			occupancy = 2/3
-#> 	impute
-#> 		class
-#> 			cls = class
-#> 			occupancy = 2/3
-#> 			seed = 1234
-#> 	transform
-#> 		TICnorm
-#>
    + 
    ## Retreive example file paths and sample information 
+file_paths <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes') %>% 
+   .[61:63]
+
+sample_information <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes') %>% 
+    dplyr::filter(name == 'QC01' | name == 'QC02' | name == 'QC03')
+
+## Detect spectral binning parameters
+bp <- binneR::detectParameters(file_paths)
+
+## Perform spectral binning
+bd <- binneR::binneRlyse(file_paths,sample_information,bp)
+#> binneR v2.6.2 Wed Nov 17 09:52:35 2021
+#> ________________________________________________________________________________
+#> Scans: 5:13 
+#> ________________________________________________________________________________
+#> Reading raw data
+#> Gathering bins
+#> Removing single scan events
+#> Averaging intensities across scans
+#> Calculating bin metrics
+#> Calculating accurate m/z
+#> Building intensity matrix
+#> Gathering file headers
+#> 
+#> Completed! [2.7S]
+
+## Detect pre-treatment parameters
+pp <- detectPretreatmentParameters(bd) 
+#> Only 1 batch detected, skipping detection
+
+pp
+#> Parameters:
+#> pre-treatment
+#> 	QC
+#> 		occupancyFilter
+#> 			cls = class
+#> 			QCidx = QC
+#> 			occupancy = 2/3
+#> 		impute
+#> 			cls = class
+#> 			QCidx = QC
+#> 			occupancy = 2/3
+#> 			parallel = variables
+#> 			seed = 1234
+#> 		RSDfilter
+#> 			cls = class
+#> 			QCidx = QC
+#> 			RSDthresh = 50
+#> 		removeQC
+#> 			cls = class
+#> 			QCidx = QC
+#> 	occupancyFilter
+#> 		maximum
+#> 			cls = class
+#> 			occupancy = 2/3
+#> 	impute
+#> 		class
+#> 			cls = class
+#> 			occupancy = 2/3
+#> 			seed = 1234
+#> 	transform
+#> 		TICnorm
+#> 
+
    diff --git a/docs/reference/exportCSV.html b/docs/reference/exportCSV.html index a381af3..118d4f3 100644 --- a/docs/reference/exportCSV.html +++ b/docs/reference/exportCSV.html @@ -72,7 +72,7 @@ metaboMisc - 0.5.7 + 0.5.8 @@ -147,8 +147,9 @@

    Details

    If the file path directory does not exist, the directory is created prior to export.

    Examples

    - 
    exportCSV(iris, "iris.csv")
-
    #> [1] "iris.csv"
    + 
    exportCSV(iris, "iris.csv")
+#> [1] "iris.csv"
+
    @@ -139,22 +139,37 @@

    Value

    A tibble containing feature summaries

    Examples

    - 
    ## Retrieve file paths and sample information for example data
-files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
-
-info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
-
-## Perform spectral binning
-analysis <- binneR::binneRlyse(files, 
-                               info, 
-                               parameters = binneR::detectParameters(files))
-
    #> binneR v2.5.3 Mon Sep 27 11:19:33 2021
    #> ________________________________________________________________________________
    #> Scans: 5:14 
    #> ________________________________________________________________________________
    #> Reading raw data
    #> Gathering bins
    #> Removing single scan events
    #> Averaging intensities across scans
    #> Calculating bin metrics
    #> Calculating accurate m/z
    #> Building intensity matrix
    #> Gathering file headers
    #> 
    #> Completed! [2.3S]
    
-featureSummary(analysis)
-
    #> # A tibble: 2 × 3
-#>   Mode     `Number of bins` `Missing Data (%)`
-#>   <chr>               <int>              <dbl>
-#> 1 Negative             1188               6.48
-#> 2 Positive             1550               9.68
    + 
    ## Retrieve file paths and sample information for example data
+files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
+
+info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
+
+## Perform spectral binning
+analysis <- binneR::binneRlyse(files, 
+                               info, 
+                               parameters = binneR::detectParameters(files))
+#> binneR v2.6.2 Wed Nov 17 09:52:39 2021
+#> ________________________________________________________________________________
+#> Scans: 5:14 
+#> ________________________________________________________________________________
+#> Reading raw data
+#> Gathering bins
+#> Removing single scan events
+#> Averaging intensities across scans
+#> Calculating bin metrics
+#> Calculating accurate m/z
+#> Building intensity matrix
+#> Gathering file headers
+#> 
+#> Completed! [3.5S]
+
+featureSummary(analysis)
+#> # A tibble: 2 × 3
+#>   Mode     `Number of bins` `Missing Data (%)`
+#>   <chr>               <int>              <dbl>
+#> 1 Negative             1188               6.48
+#> 2 Positive             1550               9.68
+
    diff --git a/docs/reference/plotRSD-1.png b/docs/reference/plotRSD-1.png index 2fb9f21..ef02af5 100644 Binary files a/docs/reference/plotRSD-1.png and b/docs/reference/plotRSD-1.png differ diff --git a/docs/reference/plotRSD-2.png b/docs/reference/plotRSD-2.png index 1ec08ff..a0fbb88 100644 Binary files a/docs/reference/plotRSD-2.png and b/docs/reference/plotRSD-2.png differ diff --git a/docs/reference/plotRSD.html b/docs/reference/plotRSD.html index febf8b5..31e0627 100644 --- a/docs/reference/plotRSD.html +++ b/docs/reference/plotRSD.html @@ -72,7 +72,7 @@ metaboMisc - 0.5.7 + 0.5.8 @@ -144,20 +144,39 @@

    Value

    A list of plots of RSD distributions

    Examples

    - 
    ## Retrieve file paths and sample information for example data
-files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
-
-info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
-
-## Perform spectral binning
-analysis <- binneR::binneRlyse(files, 
-                               info, 
-                               parameters = binneR::detectParameters(files))
-
    #> binneR v2.5.3 Mon Sep 27 11:19:36 2021
    #> ________________________________________________________________________________
    #> Scans: 5:14 
    #> ________________________________________________________________________________
    #> Reading raw data
    #> Gathering bins
    #> Removing single scan events
    #> Averaging intensities across scans
    #> Calculating bin metrics
    #> Calculating accurate m/z
    #> Building intensity matrix
    #> Gathering file headers
    #> 
    #> Completed! [3S]
    
-## Plot RSD distributions
-plotRSD(analysis)
-
    #> $n
    #> 
-#> $p
    #>
    + 
    ## Retrieve file paths and sample information for example data
+files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
+
+info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
+
+## Perform spectral binning
+analysis <- binneR::binneRlyse(files, 
+                               info, 
+                               parameters = binneR::detectParameters(files))
+#> binneR v2.6.2 Wed Nov 17 09:52:44 2021
+#> ________________________________________________________________________________
+#> Scans: 5:14 
+#> ________________________________________________________________________________
+#> Reading raw data
+#> Gathering bins
+#> Removing single scan events
+#> Averaging intensities across scans
+#> Calculating bin metrics
+#> Calculating accurate m/z
+#> Building intensity matrix
+#> Gathering file headers
+#> 
+#> Completed! [3S]
+
+## Plot RSD distributions
+plotRSD(analysis)
+#> $n
+
+#> 
+#> $p
+
+#> 
+
    @@ -150,66 +150,84 @@

    Value

    S4 object of class Analysis

    Examples

    - 
    library(metabolyseR)
-
    #> 
-#> Attaching package: ‘metabolyseR’
    #> The following object is masked from ‘package:stats’:
-#> 
-#>     anova
    #> The following objects are masked from ‘package:base’:
-#> 
-#>     raw, split
    
-## Retrieve file paths and sample information for example data
-files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
-
-info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
-
-## Detect spectral binning parameters
-bp <- binneR::detectParameters(files)
-
-## Perform spectral binning
-analysis <- binneR::binneRlyse(files, 
-                               info, 
-                               parameters = bp)
-
    #> binneR v2.5.3 Mon Sep 27 11:19:42 2021
    #> ________________________________________________________________________________
    #> Scans: 5:14 
    #> ________________________________________________________________________________
    #> Reading raw data
    #> Gathering bins
    #> Removing single scan events
    #> Averaging intensities across scans
    #> Calculating bin metrics
    #> Calculating accurate m/z
    #> Building intensity matrix
    #> Gathering file headers
    #> 
    #> Completed! [2.3S]
    
-## Declare pre-treatment parameters
-pre_treatment_parameters <- analysisParameters('pre-treatment')
-parameters(pre_treatment_parameters,
-                        'pre-treatment') <- preTreatmentParameters(
- list(
-   occupancyFilter = 'maximum',
-   impute = 'all',
-   transform = 'TICnorm'
- )
-)
-changeParameter(pre_treatment_parameters,'parallel') <- 'no'
-
-## Perform pre-treatment
-pre_treated_data <- preTreatModes(analysis,
-                                  pre_treatment_parameters)
-
    #> 
-#> metabolyseR v0.14.3 Mon Sep 27 11:19:44 2021
-#> ________________________________________________________________________________
-#> Parameters:
-#> pre-treatment
-#> 	occupancyFilter
-#> 		maximum
-#> 			cls = class
-#> 			occupancy = 2/3
-#> 	impute
-#> 		all
-#> 			occupancy = 2/3
-#> 			parallel = no
-#> 			seed = 1234
-#> 	transform
-#> 		TICnorm
-#> 
-#> ________________________________________________________________________________
-#> 
-#> Negative mode…
Negative mode 		✓ [0.5S]
-#> Positive mode…
Positive mode 		✓ [0.6S]
-#> ________________________________________________________________________________
-#> 
-#> Complete! [1.2S]
-#>
    + 
    library(metabolyseR)
+#> 
+#> Attaching package: ‘metabolyseR’
+#> The following object is masked from ‘package:stats’:
+#> 
+#>     anova
+#> The following objects are masked from ‘package:base’:
+#> 
+#>     raw, split
+
+## Retrieve file paths and sample information for example data
+files <- metaboData::filePaths('FIE-HRMS','BdistachyonEcotypes')[1:2]
+
+info <- metaboData::runinfo('FIE-HRMS','BdistachyonEcotypes')[1:2,]
+
+## Detect spectral binning parameters
+bp <- binneR::detectParameters(files)
+
+## Perform spectral binning
+analysis <- binneR::binneRlyse(files, 
+                               info, 
+                               parameters = bp)
+#> binneR v2.6.2 Wed Nov 17 09:52:51 2021
+#> ________________________________________________________________________________
+#> Scans: 5:14 
+#> ________________________________________________________________________________
+#> Reading raw data
+#> Gathering bins
+#> Removing single scan events
+#> Averaging intensities across scans
+#> Calculating bin metrics
+#> Calculating accurate m/z
+#> Building intensity matrix
+#> Gathering file headers
+#> 
+#> Completed! [2.5S]
+
+## Declare pre-treatment parameters
+pre_treatment_parameters <- analysisParameters('pre-treatment')
+parameters(pre_treatment_parameters,
+                        'pre-treatment') <- preTreatmentParameters(
+ list(
+   occupancyFilter = 'maximum',
+   impute = 'all',
+   transform = 'TICnorm'
+ )
+)
+changeParameter(pre_treatment_parameters,'parallel') <- 'no'
+
+## Perform pre-treatment
+pre_treated_data <- preTreatModes(analysis,
+                                  pre_treatment_parameters)
+#> 
+#> metabolyseR v0.14.5 Wed Nov 17 09:52:53 2021
+#> ________________________________________________________________________________
+#> Parameters:
+#> pre-treatment
+#> 	occupancyFilter
+#> 		maximum
+#> 			cls = class
+#> 			occupancy = 2/3
+#> 	impute
+#> 		all
+#> 			occupancy = 2/3
+#> 			parallel = no
+#> 			seed = 1234
+#> 	transform
+#> 		TICnorm
+#> 
+#> ________________________________________________________________________________
+#> 
+#> Negative mode…
Negative mode 		✓ [1.1S]
+#> Positive mode…
Positive mode 		✓ [0.9S]
+#> ________________________________________________________________________________
+#> 
+#> Complete! [2.1S]
+#> 
+
    @@ -157,43 +157,65 @@

    Details

    Isotope and adduct features are filtered based on the maximum intensity peak for each molecular formulas.

    Examples

    - 
    library(MFassign)
-
    #> Loading required package: ggraph
    #> Loading required package: ggplot2
    
-## Assign molecular formulas
-p <- assignmentParameters('FIE')
-
-assignment <- assignMFs(peakData,p)
-
    #> 
-#> MFassign v0.7.10 Mon Sep 27 11:19:46 2021
    #> ________________________________________________________________________________
    #> Assignment Parameters: 
-#> 
-#> 	 Technique:		 FIE 
-#> 	 Max M:			 1000 
-#> 	 Max MF score:		 5 
-#> 	 PPM threshold:		 5 
-#> 	 Relationship limit:	 0.001 
-#> 
-#> 	 Adducts: 
-#> 	 n: [M-H]1-, [M+Cl]1-, [M+K-2H]1-, [M-2H]2-, [M+Cl37]1-, [2M-H]1-
-#> 	 p: [M+H]1+, [M+K]1+, [M+Na]1+, [M+K41]1+, [M+NH4]1+, [M+2H]2+, [2M+H]1+ 
-#> 	 Isotopes: 13C, 18O, 13C2 
-#> 	 Transformations: M - [O] + [NH2], M - [OH] + [NH2], M + [H2], M - [H2] + [O], M - [H] + [CH3], M - [H] + [NH2], M - [H] + [OH], M + [H2O], M - [H3] + [H2O], M - [H] + [CHO2], M - [H] + [SO3], M - [H] + [PO3H2]
    #> ________________________________________________________________________________
    #> No. m/z:	9
    #> Calculating correlations …

    #> Calculating correlations 	✓ [24 correlations] [0.1S]
    #> Filtering correlations …

    #> Filtering correlations 		✓ [24 correlations] [0S]
    #> Preparing correlations …

    #> Preparing correlations 		✓ [0S]
    #> Calculating relationships …

    #> Calculating relationships 	✓ [7.3S]
    #> Adduct & isotope assignment …

    #> Adduct & isotope assignment 	✓ [12.2S]
    #> Transformation assignment iteration 1 …

    #> Transformation assignment iteration 1 	✓ [0S]
    #> ________________________________________________________________________________
    #> 
-#> Complete! [19.6S]
    
-## Retrieve assigned data
-assigned_data <- metabolyseR::analysisData(
- assignedData(assignment),
- tibble::tibble(sample = seq_len(nrow(peakData)))
- )
-
-reduced_data <- metaboMisc::reduce(assigned_data)
-
-reduced_data
-
    #> 
-#> AnalysisData object containing:
-#> 
-#> Samples: 60 
-#> Features: 1 
-#> Info: 1 
-#>
    + 
    library(MFassign)
+#> Loading required package: ggraph
+#> Loading required package: ggplot2
+
+## Assign molecular formulas
+p <- assignmentParameters('FIE')
+
+assignment <- assignMFs(peakData,p)
+#> 
+#> MFassign v0.7.10 Wed Nov 17 09:52:56 2021
+#> ________________________________________________________________________________
+#> Assignment Parameters: 
+#> 
+#> 	 Technique:		 FIE 
+#> 	 Max M:			 1000 
+#> 	 Max MF score:		 5 
+#> 	 PPM threshold:		 5 
+#> 	 Relationship limit:	 0.001 
+#> 
+#> 	 Adducts: 
+#> 	 n: [M-H]1-, [M+Cl]1-, [M+K-2H]1-, [M-2H]2-, [M+Cl37]1-, [2M-H]1-
+#> 	 p: [M+H]1+, [M+K]1+, [M+Na]1+, [M+K41]1+, [M+NH4]1+, [M+2H]2+, [2M+H]1+ 
+#> 	 Isotopes: 13C, 18O, 13C2 
+#> 	 Transformations: M - [O] + [NH2], M - [OH] + [NH2], M + [H2], M - [H2] + [O], M - [H] + [CH3], M - [H] + [NH2], M - [H] + [OH], M + [H2O], M - [H3] + [H2O], M - [H] + [CHO2], M - [H] + [SO3], M - [H] + [PO3H2]
+#> ________________________________________________________________________________
+#> No. m/z:	9
+#> Calculating correlations …

+#> Calculating correlations 	✓ [24 correlations] [0.1S]
+#> Filtering correlations …

+#> Filtering correlations 		✓ [24 correlations] [0S]
+#> Preparing correlations …

+#> Preparing correlations 		✓ [0S]
+#> Calculating relationships …

+#> Calculating relationships 	✓ [12.4S]
+#> Adduct & isotope assignment …

+#> Adduct & isotope assignment 	✓ [15.7S]
+#> Transformation assignment iteration 1 …

+#> Transformation assignment iteration 1 	✓ [0S]
+#> ________________________________________________________________________________
+#> 
+#> Complete! [28.2S]
+
+## Retrieve assigned data
+assigned_data <- metabolyseR::analysisData(
+ assignedData(assignment),
+ tibble::tibble(sample = seq_len(nrow(peakData)))
+ )
+
+reduced_data <- metaboMisc::reduce(assigned_data)
+
+reduced_data
+#> 
+#> AnalysisData object containing:
+#> 
+#> Samples: 60 
+#> Features: 1 
+#> Info: 1 
+#> 
+
    diff --git a/docs/reference/sanitiseTable.html b/docs/reference/sanitiseTable.html index 9029ab6..187209d 100644 --- a/docs/reference/sanitiseTable.html +++ b/docs/reference/sanitiseTable.html @@ -72,7 +72,7 @@ metaboMisc - 0.5.7 + 0.5.8 @@ -120,7 +120,7 @@

    Sanitise a data table

    Sanitise a data table by restricting the number of rows and rounding numeric columns.

    - 
    sanitiseTable(x, maxRows = 5000, sigFig = 3)
    + 
    sanitiseTable(x, maxRows = 5000, sigFig = 3, maxCharacters = 100)

    Arguments

    @@ -137,22 +137,28 @@

    Arg

    + + + +
    sigFig

    Significant figures with which to round numeric columns
    maxCharacters

    Maximum number of characters allowed in a string before it is truncated

    Examples

    - 
    sanitiseTable(iris,maxRows = 10,sigFig = 1)
-
    #> Number of rows in table restricted to 10.
    #>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-#> 1             5           4            1         0.2  setosa
-#> 2             5           3            1         0.2  setosa
-#> 3             5           3            1         0.2  setosa
-#> 4             5           3            2         0.2  setosa
-#> 5             5           4            1         0.2  setosa
-#> 6             5           4            2         0.4  setosa
-#> 7             5           3            1         0.3  setosa
-#> 8             5           3            2         0.2  setosa
-#> 9             4           3            1         0.2  setosa
-#> 10            5           3            2         0.1  setosa
    + 
    sanitiseTable(iris,maxRows = 10,sigFig = 1)
+#> Number of rows in table restricted to 10.
+#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+#> 1             5           4            1         0.2  setosa
+#> 2             5           3            1         0.2  setosa
+#> 3             5           3            1         0.2  setosa
+#> 4             5           3            2         0.2  setosa
+#> 5             5           4            1         0.2  setosa
+#> 6             5           4            2         0.4  setosa
+#> 7             5           3            1         0.3  setosa
+#> 8             5           3            2         0.2  setosa
+#> 9             4           3            1         0.2  setosa
+#> 10            5           3            2         0.1  setosa
+
    @@ -125,10 +125,10 @@

    Generate a suitable parallel processing plan

    Examples

    - 
    if (FALSE) {
-suitableParallelPlan()
-}
-
    + 
    if (FALSE) {
+suitableParallelPlan()
+}
+