inst/app/tools/help/Classifier.md

In kmezhoud/bioCancer: Interactive Multi-Omics Cancers Data Visualization and Analysis

Genes / Diseases / Pathways Classification and clustering

Classification

The classifier uses geNetClassifier methods [1] to classify genes by disease based only on gene expression (mRNA). The approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. All proccess are resumed into 5 steps: Select Studies get sample size by checking Sampling Set the sample size and the posterior probability Run classifier by checking Classification

The ranking is built by ordering the genes decreasingly by their pos- terior probability for each study (class). Each gene is assigned to a class in which has the best ranking. As a result of this process, even if a gene is found associated to several classes during the expression analysis, each gene can only be on the ranking of one class [1]. The resulting output is a table (Table 1) that associates genes to study and displays PostProb and gene expression sign exprsUpDw. The exprsMeanDiff value is the expression difference between the mean for each gene in the given class and the mean in the closest class.

Table1: Ranking Genes by Study

Plot Clusters

Gene Diseases Association

GeneList/Diseases predicts Wich disease are involving your GeneList. It uses annotations from DisGeNET [2] and Methods from clusterProfiler package [3].

The GeneList/Diseases association uses gene list as input. The assess of this prediction is based on two parameters: The number of genes that are involving in the disease (x-axis) The P-value of this association (color). In the following example, there are two annotation related to Breast cancer which involve more than 130 genes and has small P-Value.

Figure 1: Genes / Diseases Association

The Diseases Onthology uses genes/Study groups computed by Classifier (Table 1). The dotplot position indicates wish Diseases are annotated for genes/study [4]. The dot size indicates the ratio of genes involved in the disease for the same genes groups (lihc_tcga has 2/3 genes involved for the 4 disease). The color indicates the P-Value.

Figure 2: Diseases Onthology

The same process is possible with Gene Onthology (GO) and KEGG.

Figure 3: GO Pathway Enrishment

Figure 4: KEGG Pathway Enrishment

References

[1] Aibar S, Fontanillo C, Droste C, Roson-Burgo B, Campos-Laborie F, Hernandez-Rivas J and De Las Rivas J (2015). “Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles.” BMC Genomics, 16(Suppl 5:S3). http://dx.doi.org/10.1186/1471-2164-16-S5-S3.

[2] Piñero, J., Queralt-Rosinach, N., Bravo, À., Deu-Pons, J., Bauer-Mehren, A., Baron, M., Ferran Sanz, and Furlong, L. I. (2015). DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database: The Journal of Biological Databases and Curation, 2015, bav028. http://doi.org/10.1093/database/bav028

[3] Yu G, Wang L, Han Y and He Q (2012). “clusterProfiler: an R package for comparing biological themes among gene clusters.” OMICS: A Journal of Integrative Biology, 16(5), pp. 284-287. http://dx.doi.org/10.1089/omi.2011.0118.

[4] Yu G, Wang L, Yan G and He Q (2015). “DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis.” Bioinformatics, 31(4), pp. 608-609. http://dx.doi.org/10.1093/bioinformatics/btu684, http://bioinformatics.oxfordjournals.org/content/31/4/608.

kmezhoud/bioCancer documentation built on Feb. 17, 2024, 10:29 a.m.