Metabolomics data (pre-)processing workflows consist of multiple steps including peak picking, quality assessment, missing value imputation, normalisation and scaling. Several software solutions (commercial and open-source) are available for raw data processing, including r-package XCMS, to generate processed outputs in the form of a two dimensional data matrix.
These outputs contain hundreds or thousands of so called “uninformative” or “irreproducible” features. Such features could strongly hinder outputs of subsequent statistical analysis, biomarker discovery or metabolic pathway inference. Common practice is to apply peak matrix validation and filtering procedures as described in Di Guida et al. (2016), Broadhurst et al. (2018) and Schiffman et al. (2019).
Functions within the pmp
(Peak Matrix Processing)
package are designed to help users to prepare data for further
statistical data analysis in a fast, easy to use and reproducible
manner.
This vignette showcases a range of commonly applied Peak Matrix Processing steps for metabolomics datasets.
You should have R version 4.0.0 or above and Rstudio installed to be able to run this notebook.
Execute following commands from the R terminal.
Recently a review by Stanstrup et al.
(2019) reported and discussed a broad range of heterogeneous R
tools and packages that are available via Bioconductor
,
CRAN
, Github
and similar public
repositories.
pmp
package utilises SummarizedExperiment
class from Bioconductor for data input and output.
For example, outputs from widely used xcms package
can be converted to a SummarizedExperiment
object using
functions featureDefinitions
, featureValues
and pData
on the xcms
output object.
Additionally pmp
also supports any matrix-like
R
data structure (e.g. an ordinary matrix, a data frame) as
an input. If the input is a matrix-like structure pmp
will
perform several checks for data integrity. Please see section
@ref(endomorphisms) for more details.
In this tutorial we will be using a direct infusion mass spectrometry
(DIMS) dataset consisting of 172 samples measured across 8 batches and
is included in pmp
package as
SummarizedExperiemnt
class object MTBLS79
.
More detailed description of the dataset is available from Kirwan et al. (2014), MTBLS79 and R man
page.
data(MTBLS79)
MTBLS79
#> class: SummarizedExperiment
#> dim: 2488 172
#> metadata(0):
#> assays(1): ''
#> rownames(2488): 70.03364 70.03375 ... 569.36369 572.36537
#> rowData names(0):
#> colnames(172): batch01_QC01 batch01_QC02 ... Batch08_C09 Batch08_QC39
#> colData names(4): Batch Sample_Rep Class Class2
This dataset before peak matrix filtering contains 172 samples, 2488 features and 18222 missing values across all samples what is roughly around 4.2%.
Missing values in the dataset can be filtered across samples or features. The command below will remove all samples with more than 10 % missing values.
MTBLS79_filtered <- filter_samples_by_mv(df=MTBLS79, max_perc_mv=0.1)
MTBLS79_filtered
#> class: SummarizedExperiment
#> dim: 2488 170
#> metadata(2): original_data_structure processing_history
#> assays(1): ''
#> rownames(2488): 70.03364 70.03375 ... 569.36369 572.36537
#> rowData names(0):
#> colnames(170): batch01_QC01 batch01_QC02 ... Batch08_C09 Batch08_QC39
#> colData names(6): Batch Sample_Rep ... filter_samples_by_mv_perc
#> filter_samples_by_mv_flags
sum(is.na(assay(MTBLS79_filtered)))
#> [1] 17588
Missing values sample filter has removed two samples from the
dataset. Outputs from any pmp
function can be used as
inputs for another pmp
function. For example we can apply
missing value filter across features on the output of the previous call.
The function call below will filter features based on the quality
control (QC) sample group only.
MTBLS79_filtered <- filter_peaks_by_fraction(df=MTBLS79_filtered, min_frac=0.9,
classes=MTBLS79_filtered$Class, method="QC", qc_label="QC")
MTBLS79_filtered
#> class: SummarizedExperiment
#> dim: 2228 170
#> metadata(2): original_data_structure processing_history
#> assays(1): ''
#> rownames(2228): 70.03364 70.03375 ... 559.15128 569.36369
#> rowData names(2): fraction_QC fraction_flag_QC
#> colnames(170): batch01_QC01 batch01_QC02 ... Batch08_C09 Batch08_QC39
#> colData names(6): Batch Sample_Rep ... filter_samples_by_mv_perc
#> filter_samples_by_mv_flags
sum(is.na(assay(MTBLS79_filtered)))
#> [1] 11518
We can add another filter on top of the previous result. For this additional filter we will use the same function call, but this time missing values will be calculated across all samples and not only within the “QC” group.
MTBLS79_filtered <- filter_peaks_by_fraction(df=MTBLS79_filtered, min_frac=0.9,
classes=MTBLS79_filtered$Class, method="across")
MTBLS79_filtered
#> class: SummarizedExperiment
#> dim: 1957 170
#> metadata(2): original_data_structure processing_history
#> assays(1): ''
#> rownames(1957): 70.03364 70.03375 ... 559.15128 569.36369
#> rowData names(4): fraction_QC fraction_flag_QC fraction fraction_flags
#> colnames(170): batch01_QC01 batch01_QC02 ... Batch08_C09 Batch08_QC39
#> colData names(6): Batch Sample_Rep ... filter_samples_by_mv_perc
#> filter_samples_by_mv_flags
sum(is.na(assay(MTBLS79_filtered)))
#> [1] 4779
Applying these 3 filters has reduced the number of missing values from 18222 to 4779.
Another common filter approach is to filter features by the coefficient of variation (CV) or RSD% of QC samples. The example shown below will use a 30% threshold.
MTBLS79_filtered <- filter_peaks_by_rsd(df=MTBLS79_filtered, max_rsd=30,
classes=MTBLS79_filtered$Class, qc_label="QC")
MTBLS79_filtered
#> class: SummarizedExperiment
#> dim: 1386 170
#> metadata(2): original_data_structure processing_history
#> assays(1): ''
#> rownames(1386): 70.03375 70.03413 ... 527.18345 559.15128
#> rowData names(6): fraction_QC fraction_flag_QC ... rsd_QC rsd_flags
#> colnames(170): batch01_QC01 batch01_QC02 ... Batch08_C09 Batch08_QC39
#> colData names(6): Batch Sample_Rep ... filter_samples_by_mv_perc
#> filter_samples_by_mv_flags
sum(is.na(assay(MTBLS79_filtered)))
#> [1] 3040
Every function in pmp
provides a history of parameter
values that have been applied. If a user has saved outputs from an R
session, it’s also easy to check what function calls were executed.
processing_history(MTBLS79_filtered)
#> $filter_samples_by_mv
#> $filter_samples_by_mv$max_perc_mv
#> [1] 0.1
#>
#> $filter_samples_by_mv$remove_samples
#> [1] TRUE
#>
#>
#> $filter_peaks_by_fraction_QC
#> $filter_peaks_by_fraction_QC$min_frac
#> [1] 0.9
#>
#> $filter_peaks_by_fraction_QC$method
#> [1] "QC"
#>
#> $filter_peaks_by_fraction_QC$qc_label
#> [1] "QC"
#>
#> $filter_peaks_by_fraction_QC$remove_peaks
#> [1] TRUE
#>
#>
#> $filter_peaks_by_fraction_across
#> $filter_peaks_by_fraction_across$min_frac
#> [1] 0.9
#>
#> $filter_peaks_by_fraction_across$method
#> [1] "across"
#>
#> $filter_peaks_by_fraction_across$qc_label
#> [1] "QC"
#>
#> $filter_peaks_by_fraction_across$remove_peaks
#> [1] TRUE
#>
#>
#> $filter_peaks_by_rsd
#> $filter_peaks_by_rsd$max_rsd
#> [1] 30
#>
#> $filter_peaks_by_rsd$qc_label
#> [1] "QC"
#>
#> $filter_peaks_by_rsd$remove_peaks
#> [1] TRUE
Next, we will apply probabilistic quotient normalisation (PQN).
A unified function call for several commonly used missing value imputation algorithms is also included in pmp. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. In the example below we will apply knn imputation.
Within mv_imputaion
interface user can easily apply
different mehtod without worrying about input data type or tranposing
dataset.
The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features.
MTBLS79_glog <- glog_transformation(df=MTBLS79_mv_imputed,
classes=MTBLS79_filtered$Class, qc_label="QC")
glog_transformation
function uses QC samples to optimse
scaling factor lambda
. Using the function
glog_plot_plot_optimised_lambda
it’s possible to visualise
if the optimsation of the given parameter has converged at the
minima.
opt_lambda <-
processing_history(MTBLS79_glog)$glog_transformation$lambda_opt
glog_plot_optimised_lambda(df=MTBLS79_mv_imputed,
optimised_lambda=opt_lambda,
classes=MTBLS79_filtered$Class, qc_label="QC")
Functions in the pmp
package are designed to validate
input data if the user chooses not to use the SummarizedExperiment
class object.
For example, if the input matrix
consists of features
stored in columns and samples in rows or vice versa, any
function within the pmp
package will be able to handle this
in the correct manner.
peak_matrix <- t(assay(MTBLS79))
sample_classes <- MTBLS79$Class
class(peak_matrix)
#> [1] "matrix" "array"
dim(peak_matrix)
#> [1] 172 2488
class(sample_classes)
#> [1] "character"
Let’s try to use these objects as input for
mv_imputation
and filter_peaks_by_rsd
.
mv_imputed <- mv_imputation(df=peak_matrix, method="mn")
#> Warning in check_peak_matrix(df = df, classes = classes): Peak table was transposed to have features as rows and samples
#> in columns.
#>
#> There were no class labels available please check that peak table is
#>
#> still properly rotated.
#>
#> Use 'check_df=FALSE' to keep original peak matrix orientation.
rsd_filtered <- filter_peaks_by_rsd(df=mv_imputed, max_rsd=30,
classes=sample_classes, qc_label="QC")
class (mv_imputed)
#> [1] "matrix" "array"
dim (mv_imputed)
#> [1] 2488 172
class (rsd_filtered)
#> [1] "matrix" "array"
dim (rsd_filtered)
#> [1] 1630 172
Note that pmp
has automatically transposed the input
object to use the largest dimension as features, while the original R
data type matrix
has been retained also for the function
output.
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
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#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] SummarizedExperiment_1.35.1 Biobase_2.65.1
#> [3] GenomicRanges_1.57.1 GenomeInfoDb_1.41.1
#> [5] IRanges_2.39.2 S4Vectors_0.43.2
#> [7] BiocGenerics_0.51.0 MatrixGenerics_1.17.0
#> [9] matrixStats_1.3.0 pmp_1.17.0
#> [11] BiocStyle_2.33.1
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.5 impute_1.79.0 xfun_0.47
#> [4] bslib_0.8.0 ggplot2_3.5.1 lattice_0.22-6
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#> [13] highr_0.11 pkgconfig_2.0.3 Matrix_1.7-0
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#> [22] munsell_0.5.1 codetools_0.2-20 htmltools_0.5.8.1
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