Solutions

Solutions for the problems that break large studies

The failures that matter in LC/MS are not exotic. They are peak splitting, drift, batch effects, and lost low abundance signal. These pages describe each problem and how Metablify addresses it.

Peak splitting in LC/MS feature detection

Why LC/MS feature detection splits one compound into several peaks, what it costs your feature table, and how Metablify recovers the true feature.

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Retention time drift across batches

Retention time drift breaks feature matching across large LC/MS cohorts. Learn the causes and how Metablify aligns features at cohort scale.

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Batch effects and alignment in large cohorts

Batch effects can dominate a large LC/MS study before biology is visible. See how feature level alignment reduces batch structure at cohort scale.

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Low abundance feature recovery

Low abundance features are the first to be lost to noise thresholds. Learn how cohort scale evidence recovers real signal near the baseline.

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Processing studies above one thousand samples

Above a thousand samples, LC/MS processing choices that worked at small scale start to fail. See how Metablify processes large cohorts as one study.

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Prove it on your own data

Send a limited set of your existing LC/MS data and see how many additional real mass features are recovered against your current output.

Send a subset of your data and see the difference on your own study.

Send a dataset for assessment