transcriptogramStep1-method: Calculates the average of the expression values related to...
In transcriptogramer: Transcriptional analysis based on transcriptograms

Description Usage Arguments Value Author(s) References See Also Examples

For each transcriptome sample, this method assigns to each protein the average of the expression values of all the identifiers related to it. It is necessary a dictionary to map the identifiers to proteins.

transcriptogramStep1(object, expression, dictionary, nCores = 1L)

## S4 method for signature 'Transcriptogram'
transcriptogramStep1(object, expression,
  dictionary, nCores = 1L)

`object`	An object of class Transcriptogram.
`expression`	A matrix, or data.frame, containing normalized expression values from samples of microarrays or RNA-Seq (log2-counts-per-million).
`dictionary`	A matrix, or data.frame, containing two columns, the first column must contains the ENSEMBL Peptide ID, and the second column must contains values that appear as rownames in `expression`, in order to recognize the ENSEMBL Peptide ID of the other column.
`nCores`	An integer number, referring to the number of processing cores to be used; or a logical value, TRUE indicating that all processing cores should be used, and FALSE indicating the use of just one processing core. The default value of this argument is 1.

This method creates a data.frame to feed the transcriptogramS1 slot of an object of class Transcriptogram. Each row of the data.frame contains: an ENSEMBL Peptide ID, its respective position in the ordering and the mean of the expression values of the identifiers related to the same protein.

Diego Morais

da Silva, S. R. M., Perrone, G. C., Dinis, J. M., and de Almeida, R. M. C. (2014). Reproducibility enhancement and differential expression of non predefined functional gene sets in human genome. BMC Genomics.

de Almeida, R. M. C., Clendenon, S. G., Richards, W. G., Boedigheimer, M., Damore, M., Rossetti, S., Harris, P. C., Herbert, B. S., Xu, W. M., Wandinger-Ness, A., Ward, H. H., Glazier, J. A. and Bacallao, R. L. (2016). Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD. Human Genomics, 10(1), 1–24.

Ferrareze, P. A. G., Streit, R. S. A., Santos, P. R. dos, Santos, F. M. dos, de Almeida, R. M. C., Schrank, A., Kmetzsch, L., Vainstein, M. H. and Staats, C. C. (2017). Transcriptional Analysis Allows Genome Reannotation and Reveals that Cryptococcus gattii VGII Undergoes Nutrient Restriction during Infection. Microorganisms.

Morais, D. A. A., Almeida, R. M. C. and Dalmolin, R. J. S. (2019). Transcriptogramer: an R/Bioconductor package for transcriptional analysis based on protein–protein interaction. Bioinformatics.

Rybarczyk-Filho, J. L., Castro, M. A. A., Dalmolin, R. J. S., Moreira, J. C. F., Brunnet, L. G., and de Almeida, R. M. C. (2011). Towards a genome-wide transcriptogram: the Saccharomyces cerevisiae case. Nucleic Acids Research, 39(8), 3005-3016.

Xavier, L. A. da C., Bezerra, J. F., de Rezende, A. A., Oliveira, R. A. de C., Dalmolin, R. J. S., do Amaral, V. S. (2017). Analysis of genome instability biomarkers in children with non-syndromic orofacial clefts. Mutagenesis, 32(2), 313–321.

transcriptogramPreprocess, GSE9988, GPL570, Hs900, association

transcriptogram <- transcriptogramPreprocess(association, Hs900)
## Not run: 
transcriptogram <- transcriptogramStep1(transcriptogram, GSE9988, GPL570)

## End(Not run)