DOSCHEDA Report

In Doscheda: A DownStream Chemo-Proteomics Analysis Pipeline

This is a summary of the data processing and plots created in the DOSCHEDA shiny app.

Number of Channels | Number of Replicates | Input Type | Model fitted ---|---|---|--- r params$object@parameters$chans|r params$object@parameters$chans | r | r ifelse(params$object@parameters$modelType == 'sigmoid','Sigmoidal','Linear')

   # reptest <- ifelse(params$reps > 1,TRUE,FALSE)
ex<- params$object
ob_params <- getParameters(ex)
if(ob_params$reps == 1){
  reptest <- FALSE

}else{
  reptest <- TRUE
}

Figure 1: Box plot of Data Columns

boxplot(ex)

Description:

Here we should observe the means centered at approximately zero across all channels and replicates.

Figure 2: Ranked plot of proteins FC and Density plot of Data Columns

    densityPlot(ex)

Description:

These plots show the density distribution of each channel and the distribution of the ranked proteins. The Density plots should have a bell shaped distributions and be approximately centred at zero.

Figure 3: Mean vs standard deviation plot

meanSdPlot(ex)

Description:

The ranked row means versus the standard deviations for checking any dependence of the variance on the mean. The red line is a running median estimator with window-width at 10%. If this red line is approximately horizontal, this indicates no variance-mean dependence.

Figure 5: Pearson Correlation between Conditions

    corrPlot(params$object)

Description:

The Pearson correlations (r) between all the different channels are displayed. None of the channels are expected to be anti-correlated (r < 0). The QC in DOSCHEDA will highlight whether there are anti-correlated pairs of channels.

Figure 6: Principal-Component Analysis of Data columns

pcaPlot(params$object) 

Description:

Plot of two highest principal components. Replicates should (roughly) cluster together. This plot highlights if any data points are vastly 'out of place' given the experimental design.

Figure 7: Replicate vs Replicate plots for each Channel.

ob_param <- getParameters(ex)
for (i in 1:ob_param$reps) {
  for  (j in 1:ob_param$reps){
    if(i >= j ){
      next
    }else{
      for (k in 0:(ob_param$chans - 1)){
          replicatePlot(ex,conc = k,repIndex1 = i,repIndex2 = j)
      }
    }
  }
} 

Description:

Scatterplots between replicates to identify targets (drug competed proteins) and potential off-targets. Points that have high fold change in both replicates and are close to the red x = y line are considered to be targets.

if(ob_param$modelType == 'sigmoid'){
  show_text <- TRUE
} else{

  show_text <- FALSE
}

```{asis, echo=!show_text}

Linear Model Plots

The following plots are relevant if a linear model has been fitted to the data.

Figure 8: Distribution of p-values of model coefficients

```r
  plot(params$object)

```{asis, echo=!show_text} Description:

The p-value distributions for each of the model coefficients are expected to not be uniform. The QC in DOSCHEDA will highlight whether a coeffcient does not contail any significant p-values.

Figure 9: Volcano plots of model coefficients

```r
    for (i in c('slope','intercept','quadratic')){
      volcanoPlot(ex,coefficient = i)
    }

```{asis, echo=show_text}

Sigmoidal Fit Plots

The following plots are relevant when a sigmoidal model is fitted to the data. They show the top protein profiles for each of the model parameters.

```{asis, echo=show_text}
**Figure 8: Plot of the largest differences between the proteins from the lowest and highest concentrations (over 30%)**

plot(ex,sigmoidCoef = 'difference')

```{asis, echo=show_text} Description:

Plot of the proteins profiles whose difference between the top and bottom parameters of the sigmoidal model are greater than 30%.

```{asis, echo=show_text}
**Figure 9: The top proteins with significant Slope Value**

plot(ex,sigmoidCoef = 'slope')

```{asis, echo=show_text} Description:

The top 15 protein profiles with a significant slope parameter of the sigmoidal fit.

```{asis, echo=show_text}
**Figure 10: The top proteins with significant RB50 values**

 plot(ex,sigmoidCoef = 'rb50')

```{asis, echo=show_text} Description:

The top 15 protein profiles with significant RB50 parameter of the sigmoidal fit.

```

Doscheda documentation built on Nov. 8, 2020, 5:37 p.m.