Intro to AutoTuner
Craig McLean
August 1, 2019
Introduction
AutoTuner is a parameter tuning algorithm for XCMS, MZmine2, and other metabolomics data processing softwares. Using statistical inference, AutoTuner quickly finds estimates for nine distinct parameters. This guide provides an interactive example of how to use AutoTuner.
Installation
Dev Version
Currently, AutoTuner is being distributed through my github acount [here]: github.com/crmclean/Autotuner
The easiest way to download that package is by using the install_github command from the devtools package as illustrated below.
devtools::install_github("crmclean/Autotuner")Bioconductor Release
Hopefully the package will be available through bioconductor in the near future. Once this occurs, the package may be downloaded by running the following code.
if(!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Autotuner")Autotuner
This tutorial uses additional data from the package, mtbls2 This second package contains raw untargeted metabolomics data, and may be downloaded from bioconductor in a manner as the BioC installation for Autotuner.
library(Autotuner)
#> Loading required package: Biobase
#> Loading required package: BiocGenerics
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#> The following objects are masked from 'package:stats':
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#> IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
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#> Filter, Find, Map, Position, Reduce, anyDuplicated, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
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#> tapply, union, unique, unsplit, which.max, which.min
#> Welcome to Bioconductor
#>
#> Vignettes contain introductory material; view with
#> 'browseVignettes()'. To cite Bioconductor, see
#> 'citation("Biobase")', and for packages 'citation("pkgname")'.
#> Loading required package: MSnbase
#> Loading required package: mzR
#> Loading required package: Rcpp
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#> smooth
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#> This is MSnbase version 2.18.0
#> Visit https://lgatto.github.io/MSnbase/ to get started.
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#> trimws
#> ____________________________________________________________
#> / __ / / / /__ __/ _ /__ __/ / / /\ / / ____/ __ /
#> / / / / / / / / / / / / / / / / / / / \ / / /___/ /_/ /
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#> / __ / /_/ / / / / /_/ / / / / /_/ / / \ / /__/ / / /
#> /_/ /_/_____/ /_/ /_____/ /_/ /_____/_/ \ /_____/__/ /__/ v.1.6.0
#> https://github.com/crmclean/autotuner
library(mtbls2) Set Up
Input - Raw Data
AutoTuner is designed to work directly with raw mass spectral data that has been processed by using MSconvert. So far file types .mzML, .mzXML, .msData, and .netCDF have been tested and confirmed to work.
rawPaths <- c(
system.file("mzData/MSpos-Ex2-cyp79-48h-Ag-1_1-B,3_01_9828.mzData",
package = "mtbls2"),
system.file("mzData/MSpos-Ex2-cyp79-48h-Ag-2_1-B,4_01_9830.mzData",
package = "mtbls2"),
system.file("mzData/MSpos-Ex2-cyp79-48h-Ag-4_1-B,4_01_9834.mzData",
package = "mtbls2"))
if(!all(file.exists(rawPaths))) {
stop("Not all files matched here exist.")
}Here are the filetypes that will be used within this tutorial:
print(basename(rawPaths))
#> [1] "MSpos-Ex2-cyp79-48h-Ag-1_1-B,3_01_9828.mzData"
#> [2] "MSpos-Ex2-cyp79-48h-Ag-2_1-B,4_01_9830.mzData"
#> [3] "MSpos-Ex2-cyp79-48h-Ag-4_1-B,4_01_9834.mzData"Here are what the paths look like that I am entering into AutoTuner directly:
print(rawPaths)
#> [1] "/home/biocbuild/bbs-3.13-bioc/R/library/mtbls2/mzData/MSpos-Ex2-cyp79-48h-Ag-1_1-B,3_01_9828.mzData"
#> [2] "/home/biocbuild/bbs-3.13-bioc/R/library/mtbls2/mzData/MSpos-Ex2-cyp79-48h-Ag-2_1-B,4_01_9830.mzData"
#> [3] "/home/biocbuild/bbs-3.13-bioc/R/library/mtbls2/mzData/MSpos-Ex2-cyp79-48h-Ag-4_1-B,4_01_9834.mzData"Input - Metadata
AutoTuner also requires a metadata file that has at least two columns in order to derive estimates. One column should contain string matches to all the raw data files that will be processed (see above for an example). The second should contain information on the experimental factor each sample belongs to.
metadata <- read.table(system.file(
"a_mtbl2_metabolite_profiling_mass_spectrometry.txt",
package = "mtbls2"), header = TRUE, stringsAsFactors = FALSE)
metadata <- metadata[sub("mzData/", "", metadata$Raw.Spectral.Data.File) %in%
basename(rawPaths),]This is what the metadata file should look like. In our case, the column matching the raw data files is called “File.Name”, while the one with experimental factor information is called “Sample.Type”.
print(metadata)
#> Sample.Name Protocol.REF Parameter.Value.Post.Extraction.
#> 13 Ex2-cyp79-48h-Ag-1 Extraction 200 µL methanol/water, 30/70 (v/v)
#> 14 Ex2-cyp79-48h-Ag-2 Extraction 200 µL methanol/water, 30/70 (v/v)
#> 16 Ex2-cyp79-48h-Ag-4 Extraction 200 µL methanol/water, 30/70 (v/v)
#> Parameter.Value.Derivatization. Extract.Name Protocol.REF.1
#> 13 none Ex2-cyp79-48h-Ag-1 Chromatography
#> 14 none Ex2-cyp79-48h-Ag-2 Chromatography
#> 16 none Ex2-cyp79-48h-Ag-4 Chromatography
#> Parameter.Value.Chromatography.Instrument. Term.Source.REF
#> 13 ACQUITY UPLC Systems with 2D Technology MS
#> 14 ACQUITY UPLC Systems with 2D Technology MS
#> 16 ACQUITY UPLC Systems with 2D Technology MS
#> Term.Accession.Number Parameter.Value.Column.type.
#> 13 1001765 HSS T3
#> 14 1001765 HSS T3
#> 16 1001765 HSS T3
#> Parameter.Value.Chromatogram.Name. Term.Source.REF.1 Term.Accession.Number.1
#> 13 N/A NA NA
#> 14 N/A NA NA
#> 16 N/A NA NA
#> Protocol.REF.2 Parameter.Value.instrument. Term.Source.REF.2
#> 13 Mass spectrometry micrOTOF-Q MS
#> 14 Mass spectrometry micrOTOF-Q MS
#> 16 Mass spectrometry micrOTOF-Q MS
#> Term.Accession.Number.2 Parameter.Value.ion.source. Term.Source.REF.3
#> 13 NA ESI MS
#> 14 NA ESI MS
#> 16 NA ESI MS
#> Term.Accession.Number.3 Parameter.Value.analyzer. Term.Source.REF.4
#> 13 1000073 time-of-flight MS
#> 14 1000073 time-of-flight MS
#> 16 1000073 time-of-flight MS
#> Term.Accession.Number.4 MS.Assay.Name
#> 13 1000084 Ex2-cyp79-48h-Ag-1_1-B,3_01_9828
#> 14 1000084 Ex2-cyp79-48h-Ag-2_1-B,4_01_9830
#> 16 1000084 Ex2-cyp79-48h-Ag-4_1-B,4_01_9834
#> Raw.Spectral.Data.File Protocol.REF.3
#> 13 mzData/MSpos-Ex2-cyp79-48h-Ag-1_1-B,3_01_9828.mzData Data normalization
#> 14 mzData/MSpos-Ex2-cyp79-48h-Ag-2_1-B,4_01_9830.mzData Data normalization
#> 16 mzData/MSpos-Ex2-cyp79-48h-Ag-4_1-B,4_01_9834.mzData Data normalization
#> Normalization.Name Protocol.REF.4 Data.Transformation.Name
#> 13 N/A Data transformation N/A
#> 14 N/A Data transformation N/A
#> 16 N/A Data transformation N/A
#> Derived.Spectral.Data.File
#> 13 NA
#> 14 NA
#> 16 NA
#> Metabolite.Assignment.File
#> 13 a_mtbl2_metabolite_profiling_mass_spectrometry_maf.csv
#> 14 a_mtbl2_metabolite_profiling_mass_spectrometry_maf.csv
#> 16 a_mtbl2_metabolite_profiling_mass_spectrometry_maf.csv
#> Factor.Value.genotype. Term.Source.REF.5 Term.Accession.Number.5
#> 13 cyp79 NA NA
#> 14 cyp79 NA NA
#> 16 cyp79 NA NA
#> Factor.Value.replicate. Term.Source.REF.6 Term.Accession.Number.6
#> 13 Exp2 NA NA
#> 14 Exp2 NA NA
#> 16 Exp2 NA NACreating AutoTuner Object
AutoTuner first requires that user create an AutoTuner object. All future computations will be contained within this object.
The file_col argument corresponds to the string column of the metadata that matches raw data samples by name. The factorCol argument corresponds to the metadata column containing information about which experimental factor each sample belongs to.
Autotuner <- createAutotuner(rawPaths,
metadata,
file_col = "Raw.Spectral.Data.File",
factorCol = "Factor.Value.genotype.")
#> ~~~ Autotuner: Initializator ~~~
#> ~~~ Parsing Raw Data into R ~~~
#> ~~~ Extracting the Raw Data from Individual Samples ~~~
#> No TIC data was found.
#> Using integrated intensity data instead.
#> ~~~ Storing Everything in Autotuner Object ~~~
#> ~~~ The Autotuner Object has been Created ~~~Part 1:
Total Ion Current Peak Identification
The first part of AutoTuner involves the identification of peaks within the total ion current (TIC) of the samples loaded up into AutoTuner. These regions will be important later to estimate parameters from the raw data since AutoTuner assumes that they contain a greater number of real chemical measurements.
Sliding Window Analysis
To do this, the user peforms a sliding window analysis. A sliding window analysis is a simple time series analysis algorithm used to identify peaks within a time trace. The window is essentially using a moving average. From this, the algorithm asks whether the next observation to the right of the average is a peak. More on sliding window analyses can be found here.
The aim here is to identify TIC peaks. The user should prioritize finding where peaks start rather than caturing the entire peak bound. Downstream steps actually do a better job of estimating what the proper peak bounds should be.
The user should play with the lag, threshold, and influence parameters to perform the sliding window analysis. Here is what they represent relative to chromatography:
Lag - The number of chromatographic scan points used to test if next point is significance (ie the size number of points making up the moving average).
Threshold - A numerical constant representing how many times greater the intensity of an adjacent scan has to be from the scans in the sliding window to be considered significant.
Influence - A numerical factor used to scale the magnitude of a significant scan once it has been added to the sliding window.
lag <- 25
threshold<- 3.1
influence <- 0.1
signals <- lapply(getAutoIntensity(Autotuner),
ThresholdingAlgo, lag, threshold, influence)The output of the sliding window can be displayed with the plot_signals function:
plot_signals(Autotuner,
threshold,
## index for which data files should be displayed
sample_index = 1:3,
signals = signals)
rm(lag, influence, threshold)Interpreting Sliding Window Results
The figure above has two components:
- Top Plot: The chromatotgraphic trace for each sample (solid line) along with the noise associated with each sample (dashed line).
- Bottom Plot: A signal plot used to indicate which chromatographic regions have peaks.
The user should look for combinations of the three sliding window parameters that returns many narrow peaks within the signal plot. See the example above.
Autotuner will expand each of these regions to obtain improved estimates on the bounds within the isolatePeaks function below. The return_peaks arguement there represents the number of peaks to return from all detected TIC peaks for parameter estimation. This number is bounded by the total number of detected peaks by the sliding windown analysis above, so seeing more narrow peaks within the signal plot is recommended.
Autotuner <- isolatePeaks(Autotuner = Autotuner,
returned_peaks = 10,
signals = signals)Checking Peak Estimates
The peaks with expanded bounds returned from the isolatePeaks function can be rapidly checked visually using the plot_peaks function as shown below. The bounds should capture the correct ascention and descention points of each peak. If peak bounds are not satisfactory, the user should return to the sliding window analysis, and try a different conbination of the three parameters.
Remember, this whole process is only designed to isolate regions enriched in real features rather than find true peaks. The bounds don’t need to be completely perfect. Its much more important that the bounds contain some kind of chromatographic peaks rather than less dynamic regions of the chromatographic trace.
for(i in 1:5) {
plot_peaks(Autotuner = Autotuner,
boundary = 100,
peak = i)
}
Part 2:
Parameter Extraction from Individual Extracted Ion Chromatograms
In order to estimate parameters from the raw data, the user should run the EICparams function as below. The massThreshold is an absolute mass error that should be greater than the expected analytical capabilities of the mass analyzer. This part of the analysis might take a few minutes if the data used is large (~ 100 Mb per sample).
If returnPpmPlots is True, AutoTuner will return plots showing how the ppm threshold was estimated within the current working directory running Autotuner. This can be used to evaluate the magnitude of the massThreshold parameter.
## error with peak width estimation
## idea - filter things by mass. smaler masses are more likely to be
## random assosications
eicParamEsts <- EICparams(Autotuner = Autotuner,
massThresh = .005,
verbose = FALSE,
returnPpmPlots = FALSE,
useGap = TRUE)
#> Currently on sample 1
#> --- Currently on peak: 1
#> --- Currently on peak: 2
#> --- Currently on peak: 3
#> --- Currently on peak: 4
#> --- Currently on peak: 5
#> --- Currently on peak: 6
#> --- Currently on peak: 7
#> --- Currently on peak: 8
#> --- Currently on peak: 9
#> --- Currently on peak: 10
#> Currently on sample 2
#> --- Currently on peak: 1
#> --- Currently on peak: 2
#> --- Currently on peak: 3
#> --- Currently on peak: 4
#> --- Currently on peak: 5
#> --- Currently on peak: 6
#> --- Currently on peak: 7
#> --- Currently on peak: 8
#> --- Currently on peak: 9
#> --- Currently on peak: 10
#> Currently on sample 3
#> --- Currently on peak: 1
#> --- Currently on peak: 2
#> --- Currently on peak: 3
#> --- Currently on peak: 4
#> --- Currently on peak: 5
#> --- Currently on peak: 6
#> --- Currently on peak: 7
#> --- Currently on peak: 8
#> --- Currently on peak: 9
#> --- Currently on peak: 10Part 3:
Returning Estimates
All that remains now is to get what the dataset estimates are.
returnParams(eicParamEsts, Autotuner)
#> $eicParams
#> Parameters estimates Variability.Measure
#> ppm ppm 7.0170 2.7250
#> noise noise 360.0000 5.3830
#> preIntensity preIntensity 729.9000 30.4500
#> preScan preScan 2.0000 0.0000
#> snThresh snThresh 3.0030 0.6599
#> Max Peakwidth Max Peakwidth 14.9300 0.6744
#> Min Peakwidth Min Peakwidth 0.6735 0.0000
#> Measure
#> ppm Standard Deviation of all PPM Estimates
#> noise Standard Deviation of all noise Estimates
#> preIntensity Standard Deviation of all prefileter Intensity Estimates
#> preScan Standard Deviation of all scan coung Estimates
#> snThresh Standard Deviation of all s/n threshold Estimates
#> Max Peakwidth Distance between two highest estimated peak widths
#> Min Peakwidth Distance between two lowest estimated peak widths
#>
#> $ticParams
#> descriptions estimates
#> max_width max_width 11.7840
#> min_width min_width 6.7320
#> group_diff group_diff 1.8216There you have it! Running AutoTuner is now complete, and the estimates may be entered directly into XCMS to processes raw untargeted metabolomics data.
Session Info
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.2 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats4 parallel stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] mtbls2_1.21.2 Autotuner_1.6.0 MSnbase_2.18.0
#> [4] ProtGenerics_1.24.0 S4Vectors_0.30.0 mzR_2.26.0
#> [7] Rcpp_1.0.6 Biobase_2.52.0 BiocGenerics_0.38.0
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#> [13] zlibbioc_1.38.0 rlang_0.4.11 jquerylib_0.1.4
#> [16] preprocessCore_1.54.0 rmarkdown_2.8 BiocParallel_1.26.0
#> [19] stringr_1.4.0 munsell_0.5.0 compiler_4.1.0
#> [22] xfun_0.23 pkgconfig_2.0.3 pcaMethods_1.84.0
#> [25] htmltools_0.5.1.1 tidyselect_1.1.1 tibble_3.1.2
#> [28] IRanges_2.26.0 codetools_0.2-18 XML_3.99-0.6
#> [31] fansi_0.4.2 crayon_1.4.1 dplyr_1.0.6
#> [34] MASS_7.3-54 grid_4.1.0 jsonlite_1.7.2
#> [37] gtable_0.3.0 lifecycle_1.0.0 affy_1.70.0
#> [40] DBI_1.1.1 magrittr_2.0.1 MsCoreUtils_1.4.0
#> [43] scales_1.1.1 ncdf4_1.17 stringi_1.6.2
#> [46] impute_1.66.0 farver_2.1.0 affyio_1.62.0
#> [49] doParallel_1.0.16 limma_3.48.0 bslib_0.2.5.1
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