PAPi (version 1.12.0)

papiHtest: Apply ANOVA or t-test to results produced by papi.

Description

papiHtest applies ANOVA or t-test to a data frame generated by papi.

Usage

papiHtest(inputData, signif.level = 0.05, log.transform = TRUE, 
save = TRUE, folder, StatTest, output, adjust.pValue = TRUE, 
method = "bonferroni")

Arguments

inputData

when inputData is missing, a dialog box will pop up allowing the user to click-and-point to the comma separated value (CSV) file from which the data is to be read. Alternatively, inputData can take a character string naming the path to the CSV file to be read or the name of a variable (data frame) containing the data frame resultant from papi.

signif.level

A numeric value indicating the p-value cut-off. Every metabolic pathway showing a p-value higher than the signif.level will be removed from the data frame produced by papiHtest.

log.transform

A logical vector (TRUE or FALSE) defining if the data should be log-transformed before applying the statistical test.

save

A logical vector (TRUE or FALSE) defining if the results must be saved into a CSV file.

folder

When save = TRUE and folder is missing, a pop up dialog box will be presented to the user. The user can then select the directory to which the results will be saved. Alternatively, folder can take a character string naming the path to the folder where the results must be saved.

StatTest

When StatTest is missing, a t-test is performed if the input data has exactly two experimental conditions and an ANOVA is performed otherwise. Alternatively, StatTest may receive the values "ANOVA", "Anova", "anova", "A" or "a" for ANOVA, or "T-TEST", "T-test", "t-test", "t-TEST", "t" and "T" for t-test.

output

A character string indicating the name of the file containing the results. When output is missing, the CSV file produced is named according to the statistical test performed, t-test.csv or ANOVA.csv.

adjust.pValue

A logical vector indicating if p-values should be adjusted by the p.adjust.

method

A character string indicating the method used when applying p.adjust. See p.adjust.methods for possible methods. Default is 'bonferroni'.

Value

papiHtest generates a data frame as the input data, however, containing an additional column with the calculated p-values. Metabolic pathways showing a p-value higher than the signif.level are removed.

Details

The inputData must have the same format as data(papiResults). papiHtest can be applied leaving all of it arguments as default. In this case, pop up windows will let the user choose the required input data file and the folder to save results.

References

Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics.

See Also

buildDatabase, papi, papiLine and addKeggCodes.

Examples

### Building input data ####
Names <- c("Replicates", "Galactose metabolism", "Glycerolipid metabolism", 
"Carbon fixation in photosynthetic organisms", "Fatty acid biosynthesis", 
"D-Alanine metabolism")
Sample1 <- c("cond1", 200, 77, 99, 42, 29)
Sample2 <- c("cond1", 236, 72, 80, 39, 14)
Sample3 <- c("cond1", 269, 83, 89, 45, 31)
Sample4 <- c("cond2", 68, 12, NA, 6, 0.006)
Sample5 <- c("cond2", 57, 10, NA, 7, 0.1)
Sample6 <- c("cond2", 58, 13, NA, 8, 0.05) 

dataInput <- data.frame(cbind(Names, Sample1, Sample2, Sample3, Sample4, 
Sample5, Sample6), stringsAsFactors = FALSE)

### Applying papiHtest ####
papiHtestResults <- papiHtest(dataInput, save = FALSE)