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Feature request: Add Normics normalization as an alternative to MaxLFQ?
A new data normalization method was recently published. It seems to be more robust than MaxLFQ according to this publication. "Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure" https://doi.org/10.1016/j.mcpro.2022.100269
Hi Brian,
Thanks for sending this. I will forward to the group, and we will read the paper as a start. Hope FragPipe works OK for you, and we always welcome feedback.
Best Alexey
From: Brian Hampton @.> Sent: Wednesday, July 20, 2022 5:58 PM To: Nesvilab/FragPipe @.> Cc: Subscribed @.***> Subject: [Nesvilab/FragPipe] Feature request: Add Normics normalization as an alternative to MaxLFQ? (Issue #767)
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A new data normalization method was recently published. It seems to be more robust than MaxLFQ according to this publication. "Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure" https://doi.org/10.1016/j.mcpro.2022.100269
— Reply to this email directly, view it on GitHubhttps://github.com/Nesvilab/FragPipe/issues/767, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AIIMM62J36XH2T2BCF264XLVVBY53ANCNFSM54FJLJ4A. You are receiving this because you are subscribed to this thread.Message ID: @.@.>>
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Hello Alexey,
Thank you so much for your kind reply. First, my lab has standardized on using FragPipe after years of using MQ. So, yes it is working well for us.
I worry about normalization, however. Our samples are mouse vasculature from animals where a gene has been knocked down via tissue specific Cre/Lox system. Compared to control there are massive differences in the structure of the vessel wall in the KO animal. As you are well aware, traditionally, the normalization methods like MaxLFQ rely on the notion that the majority of proteins are not changing. In our system that assumption is strained. Many proteins are changing and the changes can be large. Not least of which is the knocked down protein which is itself abundantly expressed in the WT animal vessel. The intensity values for this protein trend to a 10-fold reduction in KO, however, the MaxLFQ Intensity values report that fold reduction is less. Orthogonal assays agree with the Intensity values not the MaxLFQ Intensity values as reported by IonQuant.
The “Normics” paper describes what seems to be a more rigorous method of normalization. Briefly, robustly discover the proteins in the sample whose abundances are changing the least and then use that subset for the normalization.
Very best regards, Brian
On Aug 4, 2022, at 5:12 PM, Alexey Nesvizhskii @.***> wrote:
Hi Brian,
Thanks for sending this. I will forward to the group, and we will read the paper as a start. Hope FragPipe works OK for you, and we always welcome feedback.
Best Alexey
From: Brian Hampton @.> Sent: Wednesday, July 20, 2022 5:58 PM To: Nesvilab/FragPipe @.> Cc: Subscribed @.***> Subject: [Nesvilab/FragPipe] Feature request: Add Normics normalization as an alternative to MaxLFQ? (Issue #767)
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A new data normalization method was recently published. It seems to be more robust than MaxLFQ according to this publication. "Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure" https://doi.org/10.1016/j.mcpro.2022.100269
— Reply to this email directly, view it on GitHubhttps://github.com/Nesvilab/FragPipe/issues/767, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AIIMM62J36XH2T2BCF264XLVVBY53ANCNFSM54FJLJ4A. You are receiving this because you are subscribed to this thread.Message ID: @.@.>>
Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues — Reply to this email directly, view it on GitHub https://github.com/Nesvilab/FragPipe/issues/767#issuecomment-1205772752, or unsubscribe https://github.com/notifications/unsubscribe-auth/ACFZ5IO7QE6ZVBO27JD3UATVXQW47ANCNFSM54FJLJ4A. You are receiving this because you authored the thread.
Hi Brian,
I agree that when using samples that are very different, like your case, maxLFQ is not good. For that reason, for AP-MS data (with bait IP and negative controls), what we write in reprint.int.tsv files (for subsequent SAINT scoring) is the summed protein intensity (topN in IonQuant, with N set to 1000, so basically all peptides). But I am not sure we should put many normalization steps in IonQuant itself, as it is probably easier to apply these normalization (and also missing value imputation steps, if desired) in the downstream tools. If it is easy to implement, we can try.
Best, Alexey
From: Brian Hampton @.> Sent: Tuesday, October 11, 2022 4:21 AM To: Nesvilab/FragPipe @.> Cc: Nesvizhskii, Alexey @.>; Comment @.> Subject: Re: [Nesvilab/FragPipe] Feature request: Add Normics normalization as an alternative to MaxLFQ? (Issue #767)
External Email - Use Caution Hello Alexey,
Thank you so much for your kind reply. First, my lab has standardized on using FragPipe after years of using MQ. So, yes it is working well for us.
I worry about normalization, however. Our samples are mouse vasculature from animals where a gene has been knocked down via tissue specific Cre/Lox system. Compared to control there are massive differences in the structure of the vessel wall in the KO animal. As you are well aware, traditionally, the normalization methods like MaxLFQ rely on the notion that the majority of proteins are not changing. In our system that assumption is strained. Many proteins are changing and the changes can be large. Not least of which is the knocked down protein which is itself abundantly expressed in the WT animal vessel. The intensity values for this protein trend to a 10-fold reduction in KO, however, the MaxLFQ Intensity values report that fold reduction is less. Orthogonal assays agree with the Intensity values not the MaxLFQ Intensity values as reported by IonQuant.
The “Normics” paper describes what seems to be a more rigorous method of normalization. Briefly, robustly discover the proteins in the sample whose abundances are changing the least and then use that subset for the normalization.
Very best regards, Brian
On Aug 4, 2022, at 5:12 PM, Alexey Nesvizhskii @.***mailto:***@***.***> wrote:
Hi Brian,
Thanks for sending this. I will forward to the group, and we will read the paper as a start. Hope FragPipe works OK for you, and we always welcome feedback.
Best Alexey
From: Brian Hampton @.mailto:***@***.***> Sent: Wednesday, July 20, 2022 5:58 PM To: Nesvilab/FragPipe @.mailto:***@***.***> Cc: Subscribed @.***mailto:***@***.***> Subject: [Nesvilab/FragPipe] Feature request: Add Normics normalization as an alternative to MaxLFQ? (Issue #767)
External Email - Use Caution
A new data normalization method was recently published. It seems to be more robust than MaxLFQ according to this publication. "Normics: Proteomic Normalization by Variance and Data-Inherent Correlation Structure" https://doi.org/10.1016/j.mcpro.2022.100269
— Reply to this email directly, view it on GitHubhttps://github.com/Nesvilab/FragPipe/issues/767, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AIIMM62J36XH2T2BCF264XLVVBY53ANCNFSM54FJLJ4A. You are receiving this because you are subscribed to this thread.Message ID: @.@.mailto:***@***.******@***.***>>
Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues — Reply to this email directly, view it on GitHub https://github.com/Nesvilab/FragPipe/issues/767#issuecomment-1205772752, or unsubscribe https://github.com/notifications/unsubscribe-auth/ACFZ5IO7QE6ZVBO27JD3UATVXQW47ANCNFSM54FJLJ4A. You are receiving this because you authored the thread.
— Reply to this email directly, view it on GitHubhttps://github.com/Nesvilab/FragPipe/issues/767#issuecomment-1274290235, or unsubscribehttps://github.com/notifications/unsubscribe-auth/AIIMM67K63QPYT2JHNLKPUTWCUPOHANCNFSM54FJLJ4A. You are receiving this because you commented.Message ID: @.@.>>
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