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Cited by 5,180Advanced Proteomics Techniques and ApplicationsBioinformatics and Genomic NetworksAdvanced Biosensing Techniques and Applications

Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ

Open access
Cox, Jürgen, Hein, Marco Y., Luber, Christian A., Paron, Igor, Nagaraj, Nagarjuna, Mann, Matthias·2014·Molecular & Cellular Proteomics
DOI 10.1074/mcp.m113.031591PMC PMC4159666
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Abstract

Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

AI snapshot

Written from the paper’s full text.

TL;DR

MaxLFQ is a label-free LC-MS quantification method combining delayed normalization with maximal extraction of peptide ratio information across samples. In benchmark datasets it accurately recovered known protein mixing ratios, including 3:1 proteome mixes and UPS dynamic-range ratios over several orders of magnitude.

Key findings

  • In a HeLa/E. coli proteome benchmark, MaxLFQ produced narrower ratio distributions and less overlap between human and E. coli proteins than spectral counting or summed peptide intensities, and most accurately recapitulated the expected 3-fold change; 3453 human and 1556 E. coli proteins were quantified.
  • Statistical testing by t test outperformed fold-change ranking: at precision 0.98, 72% of E. coli proteins were recalled, and at precision 95%, 88% were retrieved with the Welch modified t test.
  • For simulated smaller ratios, at 10% FDR almost all truly changing proteins were recovered at ratio 3, about half at ratio 1.6, with a strong drop below 1.6; the Q = 0.25 half-coverage point was around ratio 1.4.
  • In the UPS dynamic-range benchmark, MaxLFQ was close to expected values within 2 orders of magnitude and showed increased scatter but no systematic error above 100-fold ratios; summed intensities slightly underestimated very large ratios, and spectral counts covered 2 orders of magnitude less and systematically underestimated ratios.

Methods

  • Two benchmark datasets were used: (1) trypsin-digested HeLa and E. coli lysates mixed at 1:1 and 3:1 E. coli ratios, separated by isoelectric focusing into 24 fractions and analyzed in three replicates on an LTQ Orbitrap; (2) UPS1 and UPS2 standards spiked into E. coli peptides and analyzed in four replicates on a Q Exactive.
  • Raw data were processed with MaxQuant/Andromeda at 1% peptide and protein FDR; match-between-runs transferred identifications across LC-MS runs, increasing quantifiable isotope patterns more than 2-fold in the proteome benchmark and by 38% in the UPS benchmark.
  • MaxLFQ delayed normalization by optimizing run-specific normalization coefficients via Levenberg–Marquardt minimization of squared log fold changes, then reconstructed protein abundance profiles from pair-wise peptide ratios using median peptide ratios and least-squares fitting.

Limitations

  • Stated: MaxLFQ requires a dominant population of proteins that change minimally between conditions; in the benchmark the changing E. coli population was 31% of measured proteins, which still worked but shifted the non-changing population slightly negative.
  • Stated: sample-handling variability across months, machines, or laboratories remains a challenge; studies needing ultimate ratio accuracy or individual peptide quantification, such as post-translational modifications, will still rely on isotope labels, and clinical applications may benefit from spike-in references.
  • Stated: computational effort grows quadratically with sample number, though a fast normalization heuristic using a graph with minimum three and average six nearest neighbors gives linear scaling; large ratios show increased scatter above 100-fold. Evident from the text: validation rests on two benchmark datasets plus an in silico downscaling experiment, not broad clinical or diverse biological validation.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1074/mcp.m113.031591

Cites

80 of 80 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 16 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Spectral count ratio clouds were clearly separated only for the most abundant proteins.

    “Spectral count ratio clouds were clearly separated only for the most abundant proteins (Figs. 3A and 3D).”
    Quote in the paperSubstantial claim
  • Findingown data

    In the benchmark, human and E. coli proteins formed distinct clouds with different degrees of overlap for spectral counts, summed peptide intensities, and MaxLFQ.

    “In all cases, human and E. coli proteins formed distinct clouds, but with different degrees of overlap.”
    Quote in the paperSubstantial claim
  • Findingown data

    Spectral counting produced a systematic distortion that resulted in a general overestimation of the ratios of low-intensity proteins.

    “Furthermore, a systematic distortion was observable that resulted in a general overestimation of the ratios of low-intensity proteins.”
    Quote in the paperSubstantial claim
  • Findingcitation

    MaxLFQ has delivered excellent performance in benchmark comparisons with other software solutions.

    “It has delivered excellent performance in benchmark comparisons with other software solutions (31).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [31] Weisser, 2013, An automated pipeline for high-throughput label-free quantitative proteomics, J. Proteome Res source

      Not checked in the free run.

  • Findingown data

    Transferring identifications to other LC-MS runs by matching them to unidentified features based on masses and recalibrated retention times increased the number of quantifiable isotope patterns more than 2-fold.

    “Transferring identifications to other LC-MS runs by matching them to unidentified features based on their masses and recalibrated retention times increased the number of quantifiable isotope patterns more than 2-fold”
    Quote in the paperSubstantial claim
  • May combine two claims. 31
  • Classified as probably not a substantial claim. 27
  • The quote only partly states this claim. 3

  • May combine two claims.

    Findingown data

    In the low-intensity region, spectral counts became discrete values, and their log ratios adopted a very wide distribution with pronounced overlap of human and E. coli proteins.

    “In the low-intensity region, spectral counts became discrete values, and their log ratios adopted a very wide distribution with pronounced overlap of human and E. coli proteins.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    MaxQuant identified a total of 789,978 isotope clusters through MS/MS sequencing.

    “MaxQuant identified a total of 789,978 isotope clusters through MS/MS sequencing.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    MaxLFQ has been validated in many and diverse biological projects.

    “it has been validated in many and diverse biological projects.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The computational effort for the normalization procedure grows quadratically with the number of samples to be compared.

    “The computational effort for this procedure grows quadratically with the number of samples to be compared”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    The computational effort for constructing protein abundance profiles grows quadratically with the number of samples in which a protein is present, but is readily parallelizable at the protein level.

    “The computational effort grows quadratically with the number of samples in which a protein is present; however, it is readily parallelizable at the protein level.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    MaxLFQ algorithms can handle very large experiments of 500+ samples in a manageable computing time.

    “Our algorithms can handle very large experiments of 500+ samples in a manageable computing time.”
    Quote in the paperNot a substantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    In the proteome benchmark, 3453 human and 1556 E. coli proteins were unambiguously assignable to one species and had non-zero intensity in two out of three replicates.

    “In addition, protein groups had to be unambiguously assignable to one species; this was the case for 3453 human and 1556 E. coli proteins (supplemental Table S1).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    For a benchmark dataset with two proteomes mixed at known ratios, MaxLFQ accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins.

    “For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins.”
    Quote in the paperSubstantial claim

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    Both versions of the t test performed significantly better than ratio and Wilcoxon, and the Welch modified t test was slightly better than the standard t test.

    “Both versions of the t test performed significantly better, and the Welch modified t test was slightly better than the standard t test.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    MaxLFQ performed best by generating the narrowest distributions and most accurately recapitulating the expected fold change of three between population averages.

    “which demonstrated that MaxLFQ performed best not only by generating the narrowest distributions, but also by most accurately recapitulating the expected fold change of three between the population averages.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In the dynamic-range benchmark dataset, the authors recorded one of the largest published E. coli proteomes so far, resulting in high sequence coverage and a very narrow cloud of E. coli protein quantifications.

    “Illustrating this, in our dynamic-range benchmark dataset we recorded one of the largest published E. coli proteomes so far, resulting in a high sequence coverage and hence a very narrow cloud of E. coli protein quantifications.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    In E. coli plus UPS2 samples, 15 lower abundant human UPS proteins were never sequenced by MS/MS, but 10 of them could be identified and quantified in at least some replicates through matching to UPS1-containing samples.

    “In the sample of E. coli plus UPS2, 15 of the lower abundant human UPS proteins were never sequenced by MS/MS, but 10 of them could be identified and quantified in at least some of the replicates through matching to the UPS1-containing samples.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The large ratio stabilization feature stabilized the general ratio trend and reduced outlier sensitivity.

    “We found that this stabilized the general ratio trend and reduced the outlier sensitivity.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Spectral counts covered 2 orders of magnitude less than intensity-based methods, because there were no MS/MS events for all proteins of the lowest two abundance groups in all UPS2-plus-E. coli samples.

    “Spectral counts covered 2 orders of magnitude less than intensity-based methods, because there were no MS/MS events for all proteins of the lowest two abundance groups in all UPS2-plus-E. coli samples (Fig. 6D).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    For ratios of more than 100-fold, MaxLFQ showed increased scatter but no systematic error leading to over- or underestimation of the ratio.

    “For ratios of more than 100-fold, we detected increased scatter, but no systematic error that would lead to an over- or underestimation of the ratio (Fig. 6F).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The center of the 1:1 population is shifted to slightly negative values.

    “The center of the 1:1 population is shifted to slightly negative values.”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: A majority population of proteins exists that is not changing between the samples, so average behavior can be used as a relative standard.

  • May combine two claims.

    Findingown data

    UPS proteins are clearly separated from the narrow cloud of E. coli proteins and cluster in groups according to their relative abundances.

    “UPS proteins are clearly separated from the narrow cloud of E. coli proteins and cluster in groups according to their relative abundances.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    MaxLFQ reduced the overlap of human and E. coli populations relative to the summed intensity approach and markedly reduced the number of extreme outliers.

    “When we used our MaxLFQ algorithm, the overlap of the populations was further reduced relative to the summed intensity approach, and the number of extreme outliers was markedly reduced (Figs. 3C and 3F).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Summed intensities yielded very similar results to MaxLFQ within 2 orders of magnitude but showed a small systematic underestimation of very large ratios.

    “Summed intensities yielded very similar results within 2 orders of magnitude (Fig. 6C) but a small systematic underestimation of very large ratios (Fig. 6G).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The Wilcoxon-Mann-Whitney test performed better than ratio but had problems at low recall.

    “The Wilcoxon–Mann–Whitney test performed better but also had problems at low recall.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Rescaling the whole profile to the cumulative intensity across samples preserves the total summed intensity for a protein over all samples.

    “Then we rescale the whole profile to the cumulative intensity across samples, thereby preserving the total summed intensity for a protein over all samples”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    High mass resolution is crucial to accurate quantification because accurate determination of XICs of peptides is critical for comparison between samples.

    “Although high mass accuracy aids in the identification of peptides (42), it is the high mass resolution that is crucial to accurate quantification. This is because the accurate determination of extracted ion currents (XICs) of peptides is critical for comparison between samples (43).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [42] Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol source

      Not checked in the free run.

    • cites [43] Andersen, 2003, Proteomic characterization of the human centrosome by protein correlation profiling, Nature source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S1 (asserted). May combine two claims.

    Interpretationreasoning

    MaxLFQ uses only common peptides for pair-wise ratio determination without losing scalability for large numbers of samples.

    “We want to use only common peptides for pair-wise ratio determination without losing scalability for large numbers of samples.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    Any up-front separation of proteins or peptides potentially poses serious problems in a label-free approach, because partitioning into fractions is prone to change slightly in the analysis of different samples.

    “Conversely, any up-front separation of proteins or peptides potentially poses serious problems in a label-free approach, because the partitioning into fractions is prone to change slightly in the analysis of different samples.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    Both spectral counting and summed peptide intensities were extracted by MaxQuant, so no biases due to search engine and set of identified peptides were introduced.

    “Both were also extracted by MaxQuant, so we do not introduce biases due to the search engine and the set of identified peptides, and only benchmark conceptually different metrics of quantification.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Most label-free quantification methods require measurement of samples under uniform conditions with strict adherence to standard sample-handling procedures, with minimal fractionation and in tight temporal sequence.

    “Most methods require measurement of samples under uniform conditions with strict adherence to standard sample-handling procedures, with minimal fractionation and in tight temporal sequence.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    There is no limit on the number of samples that can be compared in label-free quantification, in contrast to the finite number of plexes available for label-based methods.

    “In addition, there is no limit on the number of samples that can be compared, in contrast to the finite number of “plexes” available for label-based methods (7).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [7] Dephoure, 2012, Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast, Sci. Signal source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisereasoning

    Due to stochastic MS/MS sequencing and differences in protein abundances across samples, peptide identifications are often missing in specific samples.

    “Due to stochastic MS/MS sequencing and differences in protein abundances across samples, peptide identifications are often missing in specific samples.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisereasoning

    When isotope labels are introduced early in the workflow, samples can be mixed, and any sample-handling issues equally affect all proteins or peptides, allowing complex biochemical workflows without loss of quantitative accuracy.

    “When isotope labels are introduced early in the workflow, samples can be mixed, and any sample-handling issues equally affect all proteins or peptides. This allows complex biochemical workflows without loss of quantitative accuracy.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Premisenone given

    The modest accuracy of label-free quantitative readouts relative to stable-isotope-based methods often prohibits their use for biological questions that require detection of small changes.

    “Finally, the modest accuracy of their quantitative readouts relative to those obtained with stable-isotope-based methods often prohibits their use for biological questions that require the detection of small changes, such as proteome changes upon stimulus.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    MaxLFQ normalization is done purely from the data obtained and without addition of external quantification standards or reliance on a fixed set of housekeeping proteins.

    “The normalization is done purely from the data obtained and without the addition of external quantification standards or reliance on a fixed set of “housekeeping” proteins.”
    Quote in the paperSubstantial claim
    • The authors state this as an assumption: A majority population of proteins exists that is not changing between the samples, so average behavior can be used as a relative standard.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    Label-free quantification is in principle applicable to any kind of sample, including materials that cannot be directly metabolically labeled.

    “Label-free quantification is in principle applicable to any kind of sample, including materials that cannot be directly metabolically labeled (for instance, many clinical samples).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    In the benchmark dataset, all human proteins should have had one-to-one ratios and all E. coli proteins should have had a ratio of three to one between replicate groups.

    “In the resulting six samples all human proteins therefore should have had one-to-one ratios and all E. coli proteins should have had a ratio of three to one between replicate groups.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    MaxLFQ is compatible with standard statistical analysis workflows.

    “it is compatible with standard statistical analysis workflows”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    MaxLFQ is fully compatible with any peptide or protein separation prior to LC-MS analysis.

    “We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    The triangular matrix containing all pair-wise protein ratios between any two samples is the maximal possible quantification information.

    “At this point we have constructed a triangular matrix containing all pair-wise protein ratios between any two samples, which is the maximal possible quantification information.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisereasoning

    High mass resolution and accuracy and high peptide identification rates contribute to the quality of label-free quantification.

    “High mass resolution and accuracy and high peptide identification rates have been key ingredients in the success of isotope-label-based methods. These factors contribute similarly to the quality of label-free quantification.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The pair-wise protein ratio is defined as the median of the peptide ratios, to protect against outliers.

    “The pair-wise protein ratio rCB (Fig. 2D) is then defined as the median of the peptide ratios, to protect against outliers.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisereasoning

    Ratios derived from individual peptide signals should be taken into account rather than a sum of intensities, because XIC ratios for each peptide are already a measurement of the protein ratio.

    “Instead, ratios derived from individual peptide signals should be taken into account, rather than a sum of intensities, because the XIC ratios for each peptide are already a measurement of the protein ratio.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisereasoning

    Chemical labeling is in principle universally applicable, but because labels are introduced later in sample processing, some robustness advantages are lost.

    “Chemical labeling (39–41) is in principle universally applicable, but because the labels are introduced later in the sample processing, some of the advantages in robustness are lost.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [39] Gygi, 1999, Quantitative analysis of complex protein mixtures using isotope-coded affinity tags, Nat. Biotechnol source

      Not checked in the free run.

    • cites [40] Ross, 2004, Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents, Mol. Cell. Proteomics source

      Not checked in the free run.

    • cites [41] Boersema, 2008, Triplex protein quantification based on stable isotope labeling by peptide dimethylation applied to cell and tissue lysates, Proteomics source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    High protein ratios in the range of several orders of magnitude typically occur in interaction proteomics experiments, where early mixing of isotope-labeled samples is usually not possible.

    “Such ratios typically occur in the context of interaction proteomics experiments (56), where early mixing of isotope-labeled samples is usually not possible and some of the principal advantages of metabolic labeling are therefore lost.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [56] Hubner, 2010, Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions, J. Cell Biol source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    The lack of improvement from s0 in the benchmark is presumably because all true ratios were either 1:1 or 1:3, whereas in real applications the true ratio distribution has a dense spectrum of small changes.

    “This is presumably because in the benchmark dataset all true ratios were either 1:1 or 1:3, whereas in real applications the true ratio distribution has a dense spectrum of small changes.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    In large pulldown datasets, the background population does not have to be the same over all samples and can be a different one in each pair-wise sample comparison in MaxLFQ.

    “In large pulldown datasets, the background population does not have to be the same over all samples and can be a different one in each pair-wise sample comparison in MaxLFQ.”
    Quote in the paperNot a substantial claim

  • The quote only partly states this claim.

    Interpretationown data

    Intensity-based approaches have a clear advantage over spectral counting.

    “This demonstrates a clear advantage of intensity-based approaches.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    The ease of use of MaxLFQ as part of MaxQuant should enable its wide adoption by nonspecialized labs.

    “The ease of use of MaxLFQ as part of the MaxQuant software suite should enable our technology to be widely adopted by nonspecialized labs as well.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Permutation-based methods have the advantage that no assumptions need to be made regarding parametric distributions of intensities or ratios.

    “The advantage of permutation-based methods is that no assumptions need to be made regarding the parametric distributions of intensities or ratios.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    The global shift of all log-ratios had no effect on subsequent tests for finding differentially expressed proteins, as they are insensitive to global shifts of all values.

    “However, this had no effect on subsequent tests for finding differentially expressed proteins, as they are all insensitive to global shifts of all values.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Permutation-based methods for calculating q-values and global FDRs are usually applied to control FDR and solve multiple hypothesis testing problems in real biological datasets.

    “As a means to control the FDR and solve potential multiple hypothesis testing problems in real biological datasets, we usually apply permutation-based methods for calculating q-values and global FDRs.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [55] Tusher, 2001, Significance analysis of microarrays applied to the ionizing radiation response, Proc. Natl. Acad. Sci. U.S.A source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Extreme ratios typically coincide with very different peptide populations identified in the samples to be compared, which can make protein ratio determination rely on very few quantification events and increases sensitivity to outliers.

    “Extreme ratios typically coincide with very different peptide populations identified in the samples to be compared: many in the sample with high protein abundance, of which only a small subset is found in the low-abundance sample. This can make the protein ratio determination rely on very few quantification events, which increases the sensitivity to outliers.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationnone given

    Applications requiring extreme robustness, such as clinical sample handling, will likely benefit from spike-in references as internal standards.

    “In addition, applications that require extreme robustness, such as sample handling in a clinical setting, will likely benefit from spike-in references that serve as internal standards.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    Biological studies that depend on ultimate accuracy of ratio readout or quantitative information about individual peptides will still rely on isotope labels.

    “Biological studies that depend on the ultimate accuracy of the ratio readout or on quantitative information about individual peptides, such as post-translationally modified ones, will still rely on isotope labels.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Label-free quantification benefits dramatically from increased depth, as it increases the number of quantifiable features present in a given LC-MS run and allows averaging over more peptides for protein quantification.

    “Label-free quantification benefits dramatically from this depth, as it increases the number of quantifiable features present in a given LC-MS run and allows averaging over more peptides for protein quantification.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Recent advances in mass spectrometer hardware have provided a boost in depth of standard analyses and enabled near-complete model proteome quantification in minimal measuring time.

    “Recent advances in mass spectrometer hardware (76, 77) have provided a boost in the depth of standard analyses and enabled near-complete model proteome quantification in minimal measuring time (6).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [76] Michalski, 2012, Ultra high resolution linear ion trap Orbitrap mass spectrometer (Orbitrap Elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes, Mol. Cell. Proteomics source

      Not checked in the free run.

    • cites [77] Michalski, 2011, Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer, Mol. Cell. Proteomics source

      Not checked in the free run.

    • cites [6] Mann, 2013, The coming age of complete, accurate, and ubiquitous proteomes, Mol. Cell source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    In datasets of practical interest, the true proportion of false positives is not known a priori.

    “In datasets of practical interest, the true proportion of false positives is not known a priori.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The determination of normalization factors for each LC-MS run by minimizing H(N) contains a sum over all pairs of samples that grows quadratically with the number of samples.

    “This quantity contains a sum running over all pairs of samples that grows quadratically with the number of samples.”
    Quote in the paperSubstantial claim
55 of 55

  • The authors state this as an assumption.

    A majority population of proteins exists that is not changing between the samples, so average behavior can be used as a relative standard.

    “We exploit the fact that the majority of the proteome typically does not change between any two conditions so that the average behavior can be used as a relative standard.”
    “MaxLFQ has the prerequisite that a majority population of proteins exists that is not changing between the samples.”
    “normalization was based on the assumption of a dominating population of non-changing proteins.”

  • The authors state this as an assumption.

    A dominant population of proteins changes minimally between experimental conditions.

    “The only prerequisite is a dominant population of proteins that change minimally between experimental conditions.”

  • The authors state this as an assumption.

    A large overlap of peptides between samples can be interpreted as a small distance between them.

    “A large overlap of peptides between each pair of nodes was interpreted as a small distance between them.”
3 of 3

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • LFQ intensities are normalized median mass spectra intensity values that allow quantification to be performed with any peptide and protein fractionation while maintaining high accuracy.

    “LFQ intensities are normalized median mass spectra intensity values that allow this quantification to be performed with any peptide and protein fractionation while maintaining high accuracy (Cox et al., 2014).”

    From Defining the Adult Neural Stem Cell Niche Proteome Identifies Key Regulators of Adult Neurogenesis (2020) · cites this paper as Cox et al., 2014

Cites

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Research works printed but not linked: 1

  1. Press, 2007

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Authors
Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N., & Mann, M.
Affiliations
Max Planck Institute of Biochemistry (DE)
Corresponding author
  • Cox, Jürgen
  • Mann, Matthias
ORCID
Venue
Molecular & Cellular Proteomics
Publisher
Elsevier BV
Published
2014-09
Volume
13
Issue
9
Pages
2513-2526
ISSN
1535-9476 (ISSN-L)
DOI
10.1074/mcp.m113.031591
PMC
PMC4159666
OpenAlex
W2170776626
Semantic Scholar
dc7f01616aea5d85e594d6b9d1113ca3bb292d28
Cited by
5,180
References
80
Citations by year
2026: 369 · 2025: 513 · 2024: 583 · 2023: 558 · 2022: 512 · 2021: 561 · 2020: 550 · 2019: 448 · 2018: 402 · 2017: 299 · 2016: 245 · 2015: 111 · 2014: 24 · 2012: 1
FWCI
97.15
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular & Cellular Proteomics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Max Planck Digital Library (submittedVersion, other-oa)
  • MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
Keywords
label-free quantification, MaxQuant
MeSH terms
Algorithms, Escherichia coli, HeLa Cells, Humans, Peptides, Proteins, Software, Proteome, Proteomics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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