Scientific Papers

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics | BMC Bioinformatics


Sample collection

Environmental samples were collected from the North Pacific Subtropical Gyre near Station ALOHA during two research cruises that targeted strong mesoscale eddy features during June/July 2017 and March/April 2018, traversing an area between 28° N, 156° W and 23° N, 161° W. An eddy dipole off the coast of Hawaii was detected using sea-level anomaly (SLA) satellite data and targeted for both a transect across the cyclonic and anticyclonic poles of the eddy dipole. The cyclonic pole of the eddy had a maximum negative SLA of − 15 cm in 2017 and − 20 cm in 2018, while the anticyclonic center reached + 24 cm in 2017 and + 21 cm in 2018. The 2017 cruise samples were taken along a transect across the eddy dipole while the 2018 cruise targeted only the center of each eddy.

Environmental samples were obtained using the onboard CTD rosette to collect water from 15 m, the deep chlorophyll maximum (DCM), and 175 m during the 2017 MESOSCOPE cruise and from 25 m and the DCM during the 2018 Falkor cruise. The DCM was determined visually from fluorometer data during the CTD downcast and Niskin bottles were tripped during the return trip to the surface. Seawater from each depth was sampled in triplicate by firing one Niskin bottle for each sample. Samples were brought to the surface and decanted into prewashed (3 × with DI, 3 × with sampled seawater) polycarbonate bottles for filtration. Samples were filtered by peristaltic pump onto 142 mm 0.2 µm Durapore filters held by polycarbonate filter holders on a Masterflex tubing line. Pressures were kept as low as possible while still producing a reasonable rate of flow through the filter, approximately 250–500 mL per minute. Samples were then removed from the filter holder using solvent-washed tweezers and placed into pre-combusted aluminum foil packets that were then flash-frozen in liquid nitrogen before being stored at − 80 °C until extraction. A methodological blank was also collected by running filtrate through a new filter and then treated identically to the samples.

Culture samples used as the validation sets for this paper have been previously described by [23] and on Metabolomics Workbench (Project ID PR001317).

Sample processing

Extraction of the environmental samples followed a modified Bligh & Dyer approach as detailed in [9]. Briefly, filters were added to PTFE centrifuge tubes with a 1:1 mix of 100 µm and 400 µm silica beads, approximately 2 mL − 20 °C Optima-grade DCM, and approximately 3 mL − 20 °C 1:1 methanol/water solution (both also Optima-grade). Extraction standards were added during this step. The samples were then bead-beaten three times, followed by triplicate washes with fresh methanol/water mixture. Samples were then dried down under clean nitrogen gas and warmed using a Fisher-Scientific Reacti-Therm module. Dried aqueous fractions were re-dissolved in 380 µL of Optima-grade water and amended with 20 µL isotope-labeled injection standards. Additional internal standards were added at this point to measure the variability introduced by chromatography and ionization, and the reconstituted fraction was syringe-filtered to remove any potential clogging material. This aqueous fraction was then aliquoted into an HPLC vial for injection on the HILIC column and diluted 1:1 with Optima-grade water. A pooled sample was created by combining 20 µL of each sample into the same HPLC vial, and a 1:1 dilution with water half-strength sample was aliquot from that to assess matrix effects and obscuring variation [9]. Also run alongside the environmental samples were two mixes of authentic standards in water and in an aliquot of the pooled sample at a variety of concentrations for quality control, annotation, and absolute concentration calculations. HPLC vials containing the samples were frozen at − 80 °C until thawing shortly before injection.

The CultureData samples were re-run from the frozen aliquots for this paper. The Pttime sample processing is documented on Metabolomics Workbench where it has been assigned Project ID PR001317.

LC conditions

For the MESOSCOPE, Falkor, and CultureData samples a SeQuant ZIC-pHILIC column (5 um particle size, 2.1 mm × 150 mm, from Millipore) was used with 10 mM ammonium carbonate in 85:15 acetonitrile to water (Solvent A) and 10 mM ammonium carbonate in 85:15 water to acetonitrile (Solvent B) at a flow rate of 0.15 mL/min. The column was held at 100% A for 2 min, ramped to 64% B over 18 min, ramped to 100% B over 1 min, held at 100% B for 5 min, and equilibrated at 100% A for 25 min (50 min total). The column was maintained at 30 °C. The injection volume was 2 µL for samples and standard mixes. When starting a batch, the column was equilibrated at the starting conditions for at least 30 min. To improve the performance of the HILIC column, we maintained the same injection volume, kept the instrument running water blanks between samples as necessary, and injected standards in a representative matrix (the pooled sample) in addition to standards in water. After each batch, the column was flushed with 10 mM ammonium carbonate in 85:15 water to acetonitrile for 20–30 min. LC conditions for the Pttime samples are documented on Metabolomics Workbench where it has been assigned Project ID PR001317.

MS conditions

Environmental metabolomic data was collected on a Thermo Q Exactive HF hybrid Orbitrap (QE) mass spectrometer. The capillary and auxiliary gas heater temperatures were maintained at 320 °C and 100 °C, respectively. The S-lens RF level was kept at 65, the H-ESI voltage was set to 3.3 kV and sheath gas, auxiliary gas, and sweep gas flow rates were set at 16, 3, and 1, respectively. Polarity switching was used with a scan range of 60–900 m/z and a resolution of 60,000. Calibration was performed every 3–4 days at a target mass of 200 m/z. DDA data was collected from the pooled samples for high-confidence annotation of knowns and unknowns. All files were then converted to an open-source mzML format and centroided via Proteowizard’s msConvert tool. For the Pttime samples, files were pulled directly from Metabolomics Workbench via Project ID PR001317 and used in their existing mzXML format.

Peakpicking, alignment, and grouping with XCMS

The R package XCMS was used to perform peakpicking, retention time correction, and peak correspondence [24, 25]. Files were loaded and run separately for each dataset using the “OnDiskMSnExp” infrastructure. Default parameters for the CentWave peakpicking algorithm were used except for: ppm, which was set to 5; peakwidth, which was widened to 20–80 s; prefilter, for which the intensity threshold was raised to \(10^{6}\); and integrate, which was set to 2 instead of 1. snthresh was set to zero because there are known issues with background estimation in this algorithm [6], and both verboseColumns and the extendLengthMSW parameter were set to TRUE. For retention time correction, the Obiwarp method was used except for the CultureData dataset, which was visually inspected and determined not to require correction [26]. For the Obiwarp algorithm, the binsize was reduced to 0.1 but all other parameters were left at their defaults or equivalents.

Peak grouping was performed on the two environmental datasets and the Pttime data with a bandwidth of 12, a minFraction of 0.1, binSize of 0.001, and minSamples of 2 but otherwise default arguments. CultureData’s minFraction was raised to 0.4 but was otherwise identical. Sample groups were constructed to consist of the biological replicates for all datasets. After peak grouping, peak filling was performed using the fillChromPeaks function with the ppm parameter set to 2.5. Finally, mass features with a retention time less than 30 s or larger than 20 min were removed to avoid interference from the initial and final solvent washes.

Manual inspection and classification

After the full XCMS workflow was completed, the mass features were visually inspected by a single qualified MS expert. For the Falkor and MESOSCOPE datasets, every mass feature was inspected, while only those features with a predicted probability of 0.9 or higher according to the two-parameter model produced above were inspected for the CultureData and Pttime datasets. Inspection consisted of plotting the raw intensity values against the corrected retention-time values for all data points within the m/z by RT bounding box determined by the most extreme values for the given feature. For this step, we decided to plot the entire feature across all files simultaneously rather than viewing each sample individually to both accelerate labeling and to more accurately represent what MS experts typically do when assessing the quality of a given mass feature (Fig. 7). We also decided to ignore missing values and linearly interpolate between known data points rather than filling with zeroes. These EICs were then shown to an MS expert for classification into one of 4 categories: Good, Bad, Ambiguous, or Stans only if the feature appeared to only show up in the standards. The inclusion of the Ambiguous category allowed us to reduce the likelihood of disagreements between MS experts, as while we expect some interpersonal overlap between Good and Ambiguous and between Ambiguous and Bad, our heuristic exploration with several qualified individuals showed minimal overlap between Good and Bad between experts. Features classified as Ambiguous or Stans only were dropped from the logistic regression fitting downstream. A few randomly-chosen features from the manually-assigned Good and Bad classifications are shown in Fig. 7.

Fig. 7
figure 7

Randomly selected ion chromatograms from both “Good” (top row) and “Bad” (bottom row) manual classifications. Plots show retention time along the x-axis in a 1-min window around the center of the feature and show measured intensity on the y. Features are from the MESOSCOPE dataset and colored by the depth from which the biological sample was taken. DCM = deep chlorophyll maximum, approximately 110 m. Mass feature identifications are provided as the title of each panel, starting with “FT” and followed by 4 digits except for the two features annotated using authentic standards run alongside: the 13C isotope of dimethylsulfonioacetate (DMS-Ac) and taurine

Peak feature extraction and metric calculation

Our process of feature engineering involved querying several MS experts in our lab about their intuition for what they thought best distinguished poor-quality MFs and noise from good ones.

The simplest metrics to calculate were summary statistics of those parameters reported directly by XCMS. These features consisted of the mean retention time (RT) of each MF and the standard deviation (SD) within the feature and the mean peak width (calculated by subtracting the max RT from the minimum) and its SD. We also calculated the mean m/z ratio and the SD in parts-per-million (PPM) by dividing each peak’s reported m/z ratio by the m/z ratio of the feature as a whole, then multiplying by one million. Mean peak area was calculated by taking the log10 of the individual areas then taking the mean, and the same process (log10 then mean) was repeated for the SD of the peak areas. XCMS’s default signal-to-noise parameter, sn, was also summarized in this way, but we only used sn values that were greater than or equal to zero and replaced any zeros with ones to avoid negative infinities after taking the log10. We also used the mean of other parameters reported by XCMS (f, scale, and lmin) as features. We additionally calculated several design-of-experiments metrics, using the number of peaks in each feature divided by the total number of files as well as the fraction of files in which a peak initially found by the peakpicker. This last metric was further subset into the fraction of samples in which a peak was initially found and the fraction of standards in which a peak was found (for those datasets in which standards were run). Finally, the coefficient of variance was estimated for the pooled sample peak areas by dividing the SD of the pooled sample peak areas by the mean of the same and additionally done in a robust way by using the median absolute deviation and median, respectively. For all of the above features, missing values were dropped silently from the summary calculations. We were unable to use any of the columns produced by enabling the verboseColumns = TRUE option in findChromPeaks because all of the values returned were NAs.

We also calculated several novel metrics from the raw m/z/RT/intensity values by extracting the data points falling within each individual peak’s m/z and RT bounding box (values between the XCMS-reported min and max) separately for each file. The data points were then linearly scaled to fall within the 0–1 range by subtracting the minimum RT and dividing by the maximum RT, then each scaled RT was fit to a beta distribution with α values of 2.5, 3, 4, and 5, and a fixed β value of 5. This approach allowed us to approximate a bell curve with increasing degrees of right-skewness and the beta distribution was chosen because it is constrained between 0 and 1 and simple and speedy to generate in R. For each α value, Pearson’s correlation coefficient (r) was calculated between the beta distribution and the raw data, with the highest value returned as a metric for how peak-shaped the data were (Fig. 8). The beta distribution with the highest r was also then used to estimate the noise level within the peak by scaling both the beta distribution probability densities and the raw data intensity values as described above, then subtracting the scaled beta distribution from the scaled intensity values, producing the residuals of the fit (Fig. 8). The signal-to-noise ratio (SNR) was calculated by dividing the maximum original peak height by the standard deviation of the residuals multiplied by the maximum height of the original peak. This method of SNR calculation allowed us to rapidly estimate the noise within the peak itself rather than relying on background estimation using data points outside the peak, which may not exist or may be influenced by additional mass signals [2]. If there were fewer than 5 data points, a missing value was returned and dropped in subsequent summary calculations. Accessing the raw data values also allowed us to calculate the proportion of “missed” scans in a peak for which an RT exists at other masses in the same sample but for which no data was produced at the selected m/z ratio, divided by the total number of scans between the min and max RTs.

Fig. 8
figure 8

Method used to calculate the metrics for the two-parameter model from the raw data via comparison to an idealized pseudo-Gaussian peak for both manually identified “Good” and “Bad” peaks. Normalization was performed by linearly scaling the raw values into the 0–1 range by subtracting the minimum value and dividing by the maximum. Peak shape similarity was measured with Pearson’s correlation coefficient and the noise level is estimated as the standard deviation of the residuals after the raw data is subtracted from the idealized peak

We additionally estimated the presence or absence of a 13C isotope using a similar method to extract the raw m/z/RT/intensity values within the peak bounding box, then searched the same RT values at an m/z delta of + 1.003355± 4 PPM. In places where more than 5 data points existed at both the original mass and the 13C mass, we again used Pearson’s correlation coefficient to estimate the similarity between the two mass traces and used a trapezoidal Riemann sum to estimate the area of the original and isotope peaks. The overall feature isotope shape similarity was calculated by taking the median of the correlation coefficients. We also calculated the correlation coefficient of the ratio of the \(\frac{{^{13} {\text{C}}}}{{^{12} {\text{C}}}}\) peak areas across multiple files, expecting that a true isotope would have a fixed \(\frac{{^{13} {\text{C}}}}{{^{12} {\text{C}}}}\) ratio. Both the isotope shape similarity and the isotope area correlation were used as metrics in the downstream analysis. Peaks for which no isotope signal was detected or had too few scans to calculate the above metrics were imputed with NA values that were again dropped in the calculation of summary statistics for the mass feature as a whole. Because these isotope metrics typically had highly skewed distributions with most values very close to one, we normalized them by taking the log10 of one minus the value.

Distributions were visually inspected using a pairs plot and highly correlated (above a Pearson’s r ~ 0.9) metrics had one of the redundant metrics removed.

Regressions and model development

We used three different multiple logistic regression models to predict the likelihood of each MF being categorized as “Good”. The first model included all metrics calculated as described above in Methods, the second contained only those parameters immediately available from the XCMS output without revisiting the raw data (the four core peak metrics m/z, RT, peak width, area and their standard deviations plus the mysterious lmin, f, and scale values as well as the fraction of peaks, samples, and standards found), and the final model was a simple two-parameter model using only the peak shape and novel SNR metrics.

In each case, we categorized each mass feature as a true positive (TP) if it was predicted to be Good and was manually classified as Good, a true negative if both predicted and classified as Bad, a false positive if predicted to be Good but manually classified as Bad, and a false negative if predicted to be Bad but was in fact manually classified as Good. This allowed us to additionally define two useful measures of success, the traditionally-defined false discovery rate (FDR, defined as 1-precision or the number of false positives divided by the total number of predicted positives) and the percentage of good features found (GFF, also known as the recall or sensitivity and defined as the number of true positives divided by the total number of actual positives).

To further explore questions of model stability and the potential for overfitting, we compared the predictions from a Falkor-trained model to a MESOSCOPE-trained model. This comparison was done in both the raw probability space as well as a rank-ordered space to test whether the most extreme likelihood (i.e., very best and very worst) MFs were consistently found to be most extreme independently of the actual likelihood predicted. For the raw probability space we compared the predictions using Pearson’s correlation coefficient, while Spearman’s rank-ordered coefficient was used for the ranked space. We additionally looked at the estimates produced by these two models and compared them with the combined model trained on both datasets combined to assess the model stability directly.

We also measured the robustness of the model under a smaller training set, emulating a situation in which only a fraction of the data was available or only a portion of the mass features had been labeled. This allowed us to test the required sample size for the different models, with a larger sample size presumably required for the models with more parameters. Because no parameter was present in all 3 models, we looked at the top 2 most significant parameters from each model: average m/z and peak shape for the full model, average m/z and the standard deviation in retention time for the XCMS model, and peak shape and SNR for the two-parameter model.

Finally, we tested whether the performance could be improved with regularized regression or random forest models. These models handle correlated variables better than ordinary least squares regression, so we also included several additional implementations of the peak shape and novel SNR parameters when summarizing across multiple files, using a max and a median of the top-three best values rather than just the overall median as well as a log-transformed version of the median peak shape calculated as \(median\left( {\log_{10} \left( {1 – r} \right)} \right)\) where \(r\) is Pearson’s correlation coefficient, as described above (Fig. 2). Cross-validation was used to select the optimal tuning parameter \(\lambda\) with glmnet package’s cv.glmnet for an elastic net penalty (α) of 0, 0.5, and 1. Random forests were implemented using the randomForest package with default settings and a factor-type response vector to ensure classification was applied rather than regression.

Application of the model to novel datasets

After exploring the different models described above and determining that the two-parameter model would likely perform most consistently on novel datasets, we applied this trained model on two additional datasets that differed significantly from the training data. The CultureData dataset was produced in the Ingalls lab like MESOSCOPE and Falkor, but represent data from a variety of phytoplankton and bacterial cultures in fresh and salt water rather than environmental samples.

The Pttime dataset was discovered on Metabolomics Workbench where it has been assigned Project ID PR001317. The data can be accessed directly via its Project https://doi.org/10.21228/M8GH6P. This project dataset consists of Phaeodactylum tricornutum cultures collected at a variety of timepoints from both pelleted cells and the released exudate. This dataset was chosen because of the similar LC–MS setup used as a benchmark for the performance that other labs with similar setups may expect to achieve using the trained model directly.

Each of these datasets was only fractionally labeled, with those MFs above the 0.9 likelihood threshold according to the two-parameter model reviewed manually and categorized. This stricter threshold was chosen because we felt less comfortable interpreting results based on mass features that were only 50% likely to be real, but did not feel the need to be so strict with this exploratory analysis that we wanted to limit it to > 99% likelihood MFs.

Using variable thresholds to determine effects on biological conclusions

We explored the implications of applying this model to the MESOSCOPE dataset at a variety of thresholds. In univariate space, we used nonparametric Kruskal–Wallis analyses of variance to measure the difference between the surface (15 m), DCM (~ 110 m), and 175 m samples because the metabolite peak areas could not be assumed to be normally distributed. These univariate tests were then controlled for multiple hypothesis testing using R’s p.adjust function with method fdr [27]. We also performed post-hoc Dunn tests provided by the rstatix package to categorize the response to depth for those mass features for which the KW test was significant, with responses falling into one of the 14 classes possible when permuting the sign and significance of the Dunn test outputs [28]. p-values obtained from the Dunn tests were not FDR controlled because it was used as a categorization tool rather than a null hypothesis test. In multivariate space, we used a permutational MANOVA (PERMANOVA) [29] provided by the vegan package’s adonis2 function to test for multivariate differences in structure of the metabolome with depth [30]. We ran multiple PERMANOVAs with a different subset of mass features included each time, corresponding to using the output from XCMS directly, likelihood thresholds of 0.01, 0.1, 0.5, 0.9, and finally only those MFs manually annotated as good.

All analyses were run in R [31], version 4.3.1, and code is available on GitHub at https://github.com/wkumler/MS_metrics.



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