Hi,

I am using *PLGEM* to detect DEG of my proteomic data sets which contain
four cell cycle phase, and of each has two replication.

> CCeSet
ExpressionSet (storageMode: lockedEnvironment)
assayData: 802 features, 8 samples
  element names: exprs
protocolData: none
phenoData
  sampleNames: S1, G21, ..., G1E2  (8 total)
  varLabels and varMetadata description:
    condictionName: conditionName
featureData: none
experimentData: use 'experimentData(object)'
Annotation:


I follow the guidelines of *PLGEM* package reference, and run the codes
several times. Curiously, I found that each time *PLGEM* detect different
proteins as differential expression.  Can anyone explain this ?


> CCfit <- plgem.fit(data=CCeSet, covariate=1, fitCondition="S", p=10,
q=0.5, plot.file =FALSE, fittingEval = TRUE, verbose = TRUE)
Fitting PLGEM...
samples extracted for fitting:
   condictionName
S1              S
S2              S
determining modelling points...
fitting data and modelling points...
done with fitting PLGEM.

Warning messages:
1: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  PLGEM slope is higher than 1
2: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  Adjusted r^2 is lower than 0.95
3: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  Pearson correlation coefficient is lower than 0.85
> ### computation of observed signal-to-noise ratios
> CCobsStn <- plgem.obsStn(data = CCeSet, covariate = 1, baselineCondition =
1,plgemFit = CCfit, verbose = TRUE)
calculating observed PLGEM-STN statistics:found 3 condition(s) to compare to
the baseline.
working on baseline S ...
S1 S2
working on condition G2 ...
G21 G22
working on condition M ...
M1 M2
working on condition G1 ...
G1E1 G1E2
done with calculating PLGEM-STN statistics.

> ## Computation of resampled signal-to-noise ratios
> CCresampledStn <- plgem.resampledStn(data = CCeSet, plgemFit = CCfit,
iterations = "automatic", verbose = TRUE)
calculating resampled PLGEM-STN statistics:found 3 condition(s) to compare
to the baseline.
baseline samples:
S1 S2
resampling on samples:
S1 S2
Using 16 iterations...
working on cases with 2 replicates...
     Iterations:
done with calculating resampled PLGEM-STN statistics.

> ## computation of p-value
> CCpValues <- plgem.pValue(observedStn = CCobsStn, plgemResampledStn =
CCresampledStn, verbose = TRUE)
calculating PLGEM p-values... done.

> ## Detection of differentially expressed proteins (DEP)
> CCdegList <- plgem.deg(observedStn = CCobsStn, plgemPval = CCpValues,
delta = 0.001, verbose = TRUE)
selecting significant DEG:found 3 condition(s) compared to the baseline.
*Delta =  0.001
        Condition =  G2_vs_S
delta: 0.001 condition: G2_vs_S found 12 DEG
        Condition =  M_vs_S
delta: 0.001 condition: M_vs_S found 34 DEG
        Condition =  G1_vs_S
delta: 0.001 condition: G1_vs_S found 71 DEG*
done with selecting significant DEG.

>

> CCfit <- plgem.fit(data=CCeSet, covariate=1, fitCondition="S", p=10,
q=0.5, plot.file =FALSE, fittingEval = TRUE, verbose = TRUE)
Fitting PLGEM...
samples extracted for fitting:
   condictionName
S1              S
S2              S
determining modelling points...
fitting data and modelling points...
done with fitting PLGEM.

Warning messages:
1: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  PLGEM slope is higher than 1
2: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  Adjusted r^2 is lower than 0.95
3: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  Pearson correlation coefficient is lower than 0.85
> ### computation of observed signal-to-noise ratios
> CCobsStn <- plgem.obsStn(data = CCeSet, covariate = 1, baselineCondition =
1,plgemFit = CCfit, verbose = TRUE)
calculating observed PLGEM-STN statistics:found 3 condition(s) to compare to
the baseline.
working on baseline S ...
S1 S2
working on condition G2 ...
G21 G22
working on condition M ...
M1 M2
working on condition G1 ...
G1E1 G1E2
done with calculating PLGEM-STN statistics.

> ## Computation of resampled signal-to-noise ratios
> CCresampledStn <- plgem.resampledStn(data = CCeSet, plgemFit = CCfit,
iterations = "automatic", verbose = TRUE)
calculating resampled PLGEM-STN statistics:found 3 condition(s) to compare
to the baseline.
baseline samples:
S1 S2
resampling on samples:
S1 S2
Using 16 iterations...
working on cases with 2 replicates...
     Iterations:
done with calculating resampled PLGEM-STN statistics.

> ## computation of p-value
> CCpValues <- plgem.pValue(observedStn = CCobsStn, plgemResampledStn =
CCresampledStn, verbose = TRUE)
calculating PLGEM p-values... done.

> ## Detection of differentially expressed proteins (DEP)
> CCdegList <- plgem.deg(observedStn = CCobsStn, plgemPval = CCpValues,
delta = 0.001, verbose = TRUE)
selecting significant DEG:found 3 condition(s) compared to the baseline.
*Delta =  0.001
        Condition =  G2_vs_S
delta: 0.001 condition: G2_vs_S found 778 DEG
        Condition =  M_vs_S
delta: 0.001 condition: M_vs_S found 790 DEG
        Condition =  G1_vs_S
delta: 0.001 condition: G1_vs_S found 793 DEG*
done with selecting significant DEG.

>
> CCfit <- plgem.fit(data=CCeSet, covariate=1, fitCondition="S", p=10,
q=0.5, plot.file =FALSE, fittingEval = TRUE, verbose = TRUE)
Fitting PLGEM...
samples extracted for fitting:
   condictionName
S1              S
S2              S
determining modelling points...
fitting data and modelling points...
done with fitting PLGEM.

Warning messages:
1: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  PLGEM slope is higher than 1
2: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  Adjusted r^2 is lower than 0.95
3: In plgem.fit(data = CCeSet, covariate = 1, fitCondition = "S", p = 10,  :
  Pearson correlation coefficient is lower than 0.85
> ### computation of observed signal-to-noise ratios
> CCobsStn <- plgem.obsStn(data = CCeSet, covariate = 1, baselineCondition =
1,plgemFit = CCfit, verbose = TRUE)
calculating observed PLGEM-STN statistics:found 3 condition(s) to compare to
the baseline.
working on baseline S ...
S1 S2
working on condition G2 ...
G21 G22
working on condition M ...
M1 M2
working on condition G1 ...
G1E1 G1E2
done with calculating PLGEM-STN statistics.

> ## Computation of resampled signal-to-noise ratios
> CCresampledStn <- plgem.resampledStn(data = CCeSet, plgemFit = CCfit,
iterations = "automatic", verbose = TRUE)
calculating resampled PLGEM-STN statistics:found 3 condition(s) to compare
to the baseline.
baseline samples:
S1 S2
resampling on samples:
S1 S2
Using 16 iterations...
working on cases with 2 replicates...
     Iterations:
done with calculating resampled PLGEM-STN statistics.

> ## computation of p-value
> CCpValues <- plgem.pValue(observedStn = CCobsStn, plgemResampledStn =
CCresampledStn, verbose = TRUE)
calculating PLGEM p-values... done.

> ## Detection of differentially expressed proteins (DEP)
> CCdegList <- plgem.deg(observedStn = CCobsStn, plgemPval = CCpValues,
delta = 0.001, verbose = TRUE)
selecting significant DEG:found 3 condition(s) compared to the baseline.
*Delta =  0.001
        Condition =  G2_vs_S
delta: 0.001 condition: G2_vs_S found 19 DEG
        Condition =  M_vs_S
delta: 0.001 condition: M_vs_S found 66 DEG
        Condition =  G1_vs_S
delta: 0.001 condition: G1_vs_S found 115 DEG*
done with selecting significant DEG.


Guangchuang Yu
--~--~---------~--~----~------------~-------~--~----~
Institutes of Life & Health Engineering
Jinan University, 601 Huangpu Ave. W.
Guangzhou 510632,  P.R. China
Tel: +86-20-85222677
Email: guangchuangyu@gmail.com
-~----------~----~----~----~------~----~------~--~---

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