metabolite library  (IROA Technologies LLC)

Journal: bioRxiv

Article Title: Signal, noise, and sampling: How pool size and replication shape metabolomic inference

doi: 10.64898/2026.04.07.717001

Figure Lengend Snippet: Points represent individual samples colored by pool size and shaped by age. PCA was performed on mean-centered metabolite values without additional scaling. Variance explained by each PC is shown in parentheses on axis labels.

Article Snippet: Metabolites were identified based on accurate mass, MS/MS spectra, isotope pattern, and retention time using PeakView and MultiQuant software (AB Sciex) compared with metabolites in the facility’s metabolite IROA library (650 standards).

Techniques:

Journal: bioRxiv

Article Title: Signal, noise, and sampling: How pool size and replication shape metabolomic inference

doi: 10.64898/2026.04.07.717001

Figure Lengend Snippet: Mean distance (± SE) between all pool-size pairs (5–100, 5–50, 50–100) is shown for each metabolite panel, coloured by strain. Individual replicate values are shown as transparent points.

Article Snippet: Metabolites were identified based on accurate mass, MS/MS spectra, isotope pattern, and retention time using PeakView and MultiQuant software (AB Sciex) compared with metabolites in the facility’s metabolite IROA library (650 standards).

Techniques:

Journal: bioRxiv

Article Title: Signal, noise, and sampling: How pool size and replication shape metabolomic inference

doi: 10.64898/2026.04.07.717001

Figure Lengend Snippet: Points represent individual samples colored by pool size and shaped by diet. PCA was performed on mean-centered metabolite values without additional scaling. Variance explained by each PC is shown in parentheses on axis labels.

Article Snippet: Metabolites were identified based on accurate mass, MS/MS spectra, isotope pattern, and retention time using PeakView and MultiQuant software (AB Sciex) compared with metabolites in the facility’s metabolite IROA library (650 standards).

Techniques:

Journal: bioRxiv

Article Title: Signal, noise, and sampling: How pool size and replication shape metabolomic inference

doi: 10.64898/2026.04.07.717001

Figure Lengend Snippet: Pairwise Euclidean distances between pool sizes in the full metabolomic space. Mean distance (± SE) between all pool-size pairs (5–100, 5–50, 50–100) is shown for each metabolite panel, colored by diet.Individual replicate values are shown as transparent points.

Article Snippet: Metabolites were identified based on accurate mass, MS/MS spectra, isotope pattern, and retention time using PeakView and MultiQuant software (AB Sciex) compared with metabolites in the facility’s metabolite IROA library (650 standards).

Techniques: Metabolomic

Journal: bioRxiv

Article Title: Signal, noise, and sampling: How pool size and replication shape metabolomic inference

doi: 10.64898/2026.04.07.717001

Figure Lengend Snippet: Retention of true and false positive metabolite detection across replicate and pool-size downsampling. The proportion of diet-associated metabolites identified under downsampling was expressed as a percentage of the reference set defined at PoolSize = 100 with full replicates (8 per diet; 100%). True positives (solid lines) represent metabolites that were significant (FDR < 0.05) in both the downsampled and reference datasets, while false positives (dashed lines) were significant only in the downsampled condition. Lines show the mean percentage across all combinations of replicate removal, and shaded ribbons indicate the interquartile range (25th–75th percentile), reflecting variability across downsampling iterations. Across all metabolite panels, reductions in replicate number and pool size led to a progressive loss of true positives, while false positives remained comparatively low, indicating reduced statistical power rather than systematic inflation of spurious detections under downsampling.

Article Snippet: Metabolites were identified based on accurate mass, MS/MS spectra, isotope pattern, and retention time using PeakView and MultiQuant software (AB Sciex) compared with metabolites in the facility’s metabolite IROA library (650 standards).

Techniques:

Journal: bioRxiv

Article Title: Signal, noise, and sampling: How pool size and replication shape metabolomic inference

doi: 10.64898/2026.04.07.717001

Figure Lengend Snippet: Metabolites were grouped into high, medium, and low effect-size bins based on tertiles of absolute diet effect sizes estimated from the full dataset (PoolSize = 100, full replicates), calculated separately within each metabolite panel. For each bin, the proportion of metabolites remaining significant (FDR < 0.05) was evaluated across all combinations of replicate downsampling and pool sizes (5, 50, and 100). Lines represent the mean fraction of metabolites remaining significant across all downsampling iterations, and shaded ribbons indicate the interquartile range (25th–75th percentile), reflecting sensitivity to which replicate populations were removed. Across all panels, metabolites with larger effect sizes exhibited greater robustness to reductions in replicate number and pool size, whereas low-effect metabolites rapidly lost significance under downsampling.

Article Snippet: Metabolites were identified based on accurate mass, MS/MS spectra, isotope pattern, and retention time using PeakView and MultiQuant software (AB Sciex) compared with metabolites in the facility’s metabolite IROA library (650 standards).

Techniques:

Journal: bioRxiv

Article Title: Signal, noise, and sampling: How pool size and replication shape metabolomic inference

doi: 10.64898/2026.04.07.717001

Figure Lengend Snippet:

Article Snippet: Metabolites were identified based on accurate mass, MS/MS spectra, isotope pattern, and retention time using PeakView and MultiQuant software (AB Sciex) compared with metabolites in the facility’s metabolite IROA library (650 standards).

Techniques: Standard Deviation

Journal: Cancer & Metabolism

Article Title: Leveraging untargeted metabolomics in combination with machine learning to uncover novel insights into bladder cancer

doi: 10.1186/s40170-026-00427-4

Figure Lengend Snippet: Metabolic and pathway alterations in MIBC compared to NMIBC. A ) Heatmap showing differentially expressed, metabolites (DEM) in MIBC (n = 41) compared to NMIBC (n = 10) patients (FDR <0.25). The color scale (z-score) indicates relative metabolite abundance: upregulated metabolites are shown in yellow, and downregulated metabolites are shown in blue. B ) Volcano plot illustrating the significance and fold changes (Log2) of metabolites in MIBC (n = 41) and NMIBC (n = 10). C ) Dot plot showing the top 10 significantly enriched hallmark pathways (refer to supplementary table 6) obtained from DEMs between MIBC (n = 41) and NMIBC (n = 10). DEMs were mapped to genes and used for pathway analysis

Article Snippet: An in-house spectral library was built using reference standards of 630 metabolite compounds from IROA Technologies.

Techniques:

Journal: Cancer & Metabolism

Article Title: Leveraging untargeted metabolomics in combination with machine learning to uncover novel insights into bladder cancer

doi: 10.1186/s40170-026-00427-4

Figure Lengend Snippet: Predicting metabolic markers in MIBC over NMIBC using parsimonious machine learning models. A ) Three machine learning classification methods: k-nearest neighbor (KNN), Random Forest (RF), and Support Vector Machines (SVM) linear were used to predict MIBC (n = 41) over NMIBC (n = 10); One hundred cross-validation iterations were performed, using 80% of the data as training and 20% as testing. The median area under the receiver operating characteristic curve (AUROC) of each model is listed for each method. B ) Minimal feature classifiers were identified by selecting metabolite features informative in ≥70% of 100 iterations. SVM classification was performed with an increasing number of features to predict MIBC over NMIBC. Each green dot represents the median AUROC for a given number of metabolites. C ) The top 20 informative metabolites are plotted and sorted in decreasing order by important features in MIBC over NMIBC. D ) Lollipop plot shows the fold changes of the top 20 metabolites in MIBC over NMIBC, and data derived from Figure 3B. Detailed statistical analyses, including significance values for these metabolites, are presented in Supplementary Data 2

Article Snippet: An in-house spectral library was built using reference standards of 630 metabolite compounds from IROA Technologies.

Techniques: Plasmid Preparation, Biomarker Discovery, Derivative Assay