Corpus ID: 4804172

GOexpress: identify and visualise robust gene ontology signatures through supervised classification of gene expression data

@inproceedings{Magee2016GOexpressIA,
  title={GOexpress: identify and visualise robust gene ontology signatures through supervised classification of gene expression data},
  author={David A. Magee and Nicolas C. Nalpas and Andrew C. Parnell and Stephen Vincent Gordon and David E. MacHugh},
  year={2016}
}
3 Quick start 4 3.1 Input data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Main analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2.1 Preparing the grouping factor to analyse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2.2 Running the random forest algorithm using local annotations . . . . . . . . . . . . . . . . . . 5 3.2.3 Important notes in the absence of… Expand

Figures from this paper

References

SHOWING 1-6 OF 6 REFERENCES
How well does each gene in the dataset classify predefined groups of samples?
  • The random forest consists of multiple decision trees. Each tree is built based on a bootstrap sample (sample with replacement) of observations and a random sample of variables. The randomForest package first calculates the Gini index
  • 1984
20.0 loaded via a namespace (and not attached)
  • 20.0 loaded via a namespace (and not attached)
BiocStyle_2
  • BiocStyle_2
Rcpp_0.12.7 AnnotationDbi_1.36.0 magrittr_1.5 IRanges_2
  • Rcpp_0.12.7 AnnotationDbi_1.36.0 magrittr_1.5 IRanges_2
UTF-8 LC_IDENTIFICATION=C attached base packages: [1] parallel grid stats graphics grDevices utils datasets methods [9] base other attached packages
  • UTF-8 LC_IDENTIFICATION=C attached base packages: [1] parallel grid stats graphics grDevices utils datasets methods [9] base other attached packages
UTF-8 LC_NAME=C LC_ADDRESS=C
  • UTF-8 LC_NAME=C LC_ADDRESS=C