In danro9685/OncoScore: A tool to identify potentially oncogenic genes

The OncoScore analysis consists of two parts. One can estimate a score to asses the oncogenic potential of a set of genes, given the lecterature knowledge, at the time of the analysis, or one can study the trend of such score over time.

We next present the two analysis and we conclude with showing the capabilities of the tool to visualize the results.

First we load the library.

library("OncoScore")

The query that we show next retrieves from PubMed the citations, at the time of the query, for a list of genes in cancer related and in all the documents.

query = perform.query(c("ASXL1","IDH1","IDH2","SETBP1","TET2"))

OncoScore provides a function to merge gene names if requested by the user. This function is useful when there are aliases in the gene list.

combine.query.results(query, c('IDH1', 'IDH2'), 'new_gene')

OncoScore also provides a function to retireve the names of the genes in a given portion of a chromosome that can be exploited if we are dealing, e.g., with copy number alterations hitting regions rather than specific genes.

chr13 = get.genes.from.biomart(chromosome=13,start=54700000,end=72800000)

Furthermore, one can also automatically perform the OncoScore analysis on chromosomic regions as follows:

result = compute.oncoscore.from.region(10, 100000, 500000)

We now compute a score for each of the genes, to estimate their oncogenic potential.

result = compute.oncoscore(query)

The query that we show next retrieves from PubMed the citations, at specified time points, for a list of genes in cancer related and in all the documents.

query.timepoints = perform.query.timeseries(c("ASXL1","IDH1","IDH2","SETBP1","TET2"),
    c("2012/03/01", "2013/03/01", "2014/03/01", "2015/03/01", "2016/03/01"))

We now compute a score for each of the genes, to estimate their oncogenic potential at specified time points.

result.timeseries = compute.oncoscore.timeseries(query.timepoints)

We next plot the scores measuring the oncogenetic potential of the considered genes as a barplot.

plot.oncoscore(result, col = 'darkblue')

We finally plot the trend of the scores over the considered times as absolute and values and as variations.

plot.oncoscore.timeseries(result.timeseries)

plot.oncoscore.timeseries(result.timeseries, incremental = TRUE, ylab='absolute variation')

plot.oncoscore.timeseries(result.timeseries, incremental = TRUE, relative = TRUE, ylab='relative variation')

danro9685/OncoScore documentation built on April 28, 2024, 2:01 p.m.