View source: R/designSampleSize.R

designSampleSizePlots | R Documentation |

To illustrate the relationship of desired fold change and the calculated
minimal number sample size which are (1) number of biological replicates per condition,
(2) number of peptides per protein,
(3) number of transitions per peptide, and
(4) power. The input is the result from function (`designSampleSize`

.

```
designSampleSizePlots(data, isPlotly = FALSE)
```

`data` |
output from function designSampleSize. |

`isPlotly` |
Parameter to use Plotly or ggplot2. If set to TRUE, MSstats will save Plotly plots as HTML files. If set to FALSE MSstats will save ggplot2 plots as PDF files |

Data in the example is based on the results of sample size calculation from function `designSampleSize`

Plot for estimated sample size with assigned variable.

Meena Choi, Ching-Yun Chang, Olga Vitek.

```
# Based on the results of sample size calculation from function designSampleSize,
# we generate a series of sample size plots for number of biological replicates, or peptides,
# or transitions or power plot.
QuantData<-dataProcess(SRMRawData)
head(QuantData$ProcessedData)
## based on multiple comparisons (T1 vs T3; T1 vs T7; T1 vs T9)
comparison1<-matrix(c(-1,0,1,0,0,0,0,0,0,0),nrow=1)
comparison2<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
comparison3<-matrix(c(-1,0,0,0,0,0,0,0,1,0),nrow=1)
comparison<-rbind(comparison1,comparison2, comparison3)
row.names(comparison)<-c("T3-T1","T7-T1","T9-T1")
colnames(comparison)<-unique(QuantData$ProteinLevelData$GROUP)
testResultMultiComparisons<-groupComparison(contrast.matrix=comparison, data=QuantData)
# plot the calculated sample sizes for future experiments:
# (1) Minimal number of biological replicates per condition
result.sample<-designSampleSize(data=testResultMultiComparisons$FittedModel, numSample=TRUE,
desiredFC=c(1.25,1.75), FDR=0.05, power=0.8)
designSampleSizePlots(data=result.sample)
# (2) Power
result.power<-designSampleSize(data=testResultMultiComparisons$FittedModel, numSample=2,
desiredFC=c(1.25,1.75), FDR=0.05, power=TRUE)
designSampleSizePlots(data=result.power)
```

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