FAQ table of contents
+-
+
- Why +does the table in experimental design blink during edition? +
- How to build a +valid experimental design? +
- Why +do the items of the contextual menus for plots remain +‘undefined’? +
- Why does my +volcano plot look so aligned? +
- How to +recover differential analysis results? +
- Why +I cannot adjust the logFC threshold on each differential analysis +comparison? +
- How does “Push p-values” +works? +
- How should +I tune the calibration plot options? +
- Why +are the adjusted p-values hidden in the tabular outputs of the +differential analysis? +
- How the cells +metadata tags are structured? +
- How the cells +metadata tags are aggregated? +
Why does the table in experimental design blink during edition?
+When you edit the experimental design (during converting a text file +to MSnset or during the update of the design), it may happen that the +cells begin to blink in a random order. Then, no more operation is +possible in the table. This happens if you edit the cells too fast with +respect to the table update speed. We apologize for this caveat : this +is a known bug of the package used to provide the table. No patch is +available yet. The only workaround is to close then reopen Prostar.
+How to build a valid experimental design?
+In Prostar, the differential analysis is devoted to the processing of +hierarchical unpaired experimental designs. However, in former versions, +this was not explicit enough, so that users with paired samples could +used Prostar with wrong assumptions. To clear this out, we have changed +the experimental design construction step so that its explicitly appears +unpaired.
+As a result, the samples must now be numbered as in the following +example:
+-
+
- Condition 1: 1 - 2 - 3 - 4, +
- Condition 2: 5 - 6 - 7 - 8 +
As opposed to:
+-
+
- Condition 1: 1 - 2 - 3 - 4, +
- Condition 2: 1 - 2 - 3 - 4 +
Which, depending on the context, could suggest that the 8 samples +comes only from 4 different biological subjects, and thus leading to +paired tests - for instance, patients that are compared between Before +(Condition 1) and After (Condition 2) some treatment.
+However, one should note that even if the experimental design now +looks different, this is just due to a numbering convention, and the +statistical test is not impacted.
+Why does my volcano plot look so aligned?
+
In very uncommun situations, one may obtain a bowl shape volcano plot +such as depicted above. This is due to using Limma on a dataset for +which it is not adapted: Briefly, the numerical values in the +quantitative matrix appears to have a repetitive pattern that prevent +Limma routines to compute the number of degrees of freedom of the Chi2 +distribution on which the protein variances should be fitted. As a +result, Limma returns a result directly proportional to the fold-change, +and the p-values are none-informative. In such cases, which are +fortunately extremely odd, we advise to replace Limma test by a +classical t-test.
+How to recover differential analysis results?
+From Prostar 1.14, the differential analysis results are not exported +anymore when using the “Export fo file” functionality, regardless the +format (MSnset, excel or zipped CSV). This is due to the separate +management of the “data mining” and “data processing” outputs. As a +result, after performing the differential analysis, the results must be +downloaded thanks to the devoted buttons (otherwise, they will be lost +when closing the Prostar session). However, all the p-value +computatations (from the “hypothesis testing” menu) can be exported and +recovered from one session to another one.
+Why I cannot adjust the logFC threshold on each differential +analysis comparison?
+Shortly, because from a statistical viewpoint, doing so roughly +amount to FDR cheating. We have observed that numerous practitioners use +the logFC threshold as a way to discard some proteins on the volcano +plot, so that other proteins of interests appear more strikingly. In +addition to be an uncontrolled and subjective way of sorting the +proteins regardless of p-values, it has an important side effect on FDR +computation: FDR computation requires a sufficiently large amount of +proteins “below” the horizontal threshold on the volcano plot. However, +all the proteins filtered out because of a too low logFC are not +considered in FDR computation, so that tuning the logFC threshold to a +too high value (so as to fine tune the protein selection) may lead to a +spurious FDR. Finally, in case of more than two conditions (say A, B and +C) , it would not make sense to defined differently (i.e. with a +different logFC threshold) differentialy abundant proteins across +various comparisons (e.g. when comparing AvsB, BvsC and CvsA). For all +these reasons, we advise Prostar user to define once and for all the +logFC threshold of each proteomics experiment (to a minimal value, such +that below the threshold, a protein cannot be interesting froma +biological viewpoint, because de FC cannot be properly exploited). More +detailed explanations can be found in the following articles:
+-
+
- Q.
+Giai Gianetto, Y. Coute, C. Bruley and T. Burger. Uses and misuses
+of the fudge factor in quantitative discovery proteomics.
+Proteomics, 16(14):1955-60, 2016.
+
+ - T. +Burger. Gentle introduction to the statistical foundations of false +discovery rate in quantitative proteomics. Journal of Proteome +Research, 17(1):12-22, 2017. +
- S. +Wieczorek, Q. Giai Gianetto, T. Burger. Five simple yet essential +steps to correctly estimate the rate of false differentially abundant +proteins in mass spectrometry analyses. Journal of +Proteomics, vol 207, p. 103441, In Press, 2019 (Section +2). +
How does “Push p-values” works?
+This is now clearly explained and illustrated in +Prostar +user manual as well as in the following article (Section 3):
+ +How should I tune the calibration plot options?
+This is now clearly explained and illustrated in +Prostar +user manual as well as in the following article (Sections 4, 5 and +6):
+ +How the cells metadata tags are structured?
+Prostar prior to 1.30.0
+Peptide-level vocabulary
+──Any
+ │── 1.0 Quantitative Value
+ │ │── 1.1 Identified
+ │ │── 1.2 Recovered
+ │
+ │── 2.0 Missing value
+ │ │── 2.1 Missing POV
+ │ │── 2.2 Missing MEC
+ │
+ │── 3.0 Imputed value
+ │ │── 3.1 Imputed POV
+ │ │── 3.2 Imputed MEC
+Protein-level vocabulary:
+──Any
+ │── 1.0 Quantitative Value
+ │ │── 1.1 Identified
+ │ │── 1.2 Recovered
+ │
+ │── 2.0 Missing value
+ │ │── 2.1 Missing POV
+ │ │── 2.2 Missing MEC
+ │
+ │── 3.0 Imputed value
+ │ │── 3.1 Imputed POV
+ │ │── 3.2 Imputed MEC
+ │
+ │── 4.0 Combined valueProstar from 1.31.2
+
How the cells metadata tags are aggregated?
+A set of rules define how the cells metadata tags are aggregated: +Each example below represent a set of three peptides which are +aggregated into one protein.
+Note: Before an aggregation, a dataset does not contain:
+-
+
- x.0 tags: ‘Quantified’ (1.0), ‘Missing’ (2.0) nor ‘Imputed’ +(3.0) +
- ‘Combined tags’ tag +
These tags are specific to the aggregation process and appear only in +aggregated proteins.
+Rule 1: Aggregation of a unique type of quantified values +(among 1.1, 1.2). If the type of all the peptides to agregate +is either 1.1 or 1.2, then the resulting metadata tag is set to the +corresponding tag
+Rule 2: Aggregation of a unique type of missing values (among +2.1, 2.2). The aggregation of 2.1 peptides between each other +gives a generic missing value (2.0)
+Rule 3: Aggregation of a unique type of imputed values (among +3.3, 3.2). If the type of all the peptides to agregate is +either 3.0, 3.1 or 3.2, then the resulting metadata tag is set to an +‘Imputed’ value (3.0).
+Rule 4: Aggregation of a mix of quantified values +(1.x). If the set of cell matadata tags to agregate is a mix of +1.x, then the final metadata is set to 1.0.
+Rule 5: Aggregation of a mix of missing values +(2.x). If the set of metacell to agregate is a mix of 2.x, then +the final metadata is set to ‘Missing’ (2.0)
+Rule 6: Aggregation of a mix of imputed values +(3.x). If the set of cells metadata tags to agregate is a mix +of 3.x, then the final metadata is set to ‘Imputed’ (3.0)
+Rule 7: Aggregation of a mix of missing values (2.x) with any +of quantitative and/or imputed values (1.x, 3.x). This case is +not possible.
+Rule 8: Agregation of a mix of quantitative values (1.x) and +imputed values (3.x). If the set of metacell to agregate is a +mix of 3.X and 3.0 and other (1.X), then the final metadata is set to +‘Combined tags’ (4.0).
+
Once the cell metadata tags have been aggregated, the final step of +the process consist in analyzing missing values in order to identify +‘missing POV’ and ‘missing MEC’. Thus, at the end of the agregation +process, a dataset may contains
+