ROC plotter using patient data:ROC Plotter
for breast cancer ROC Plotter
for ovarian cancer ROC Plotter
for glioblastoma ROC Plotter
for colorectal cancer ROC Plotter
for immunotherapy In development:
ROC plotter using in vitro data:ROC Plotter
for cell lines
ROC analyis of user-uploaded data:Generate ROC Plot
for uploaded data
What is the ROC Plotter?
The ROC plotter is capable to link gene expression and response to therapy using transcriptome-level data of breast, ovarian, and colorectal cancer patients and glioblastomas. The custom plotter generates a ROC plot for user-uploaded data.
What is a biomarker?
A biomarker can be either predictive or prognostic. A predictive marker predicts benefit from a specific treatment; it helps to select a particular treatment over another. A prognostic marker predicts the natural history of disease (survival), independent of treatment. It can indicate a need for further treatment, but does not help to determine which treatment. The ROC Plotter is the first online transcriptome-level validation tool for predictive biomarkers.
What is the ROC Plotter useful for:
Which genes can I use?
ROC plotter recognizes 70,632 gene symbols including HUGO Gene Nomenclature Committee approved official gene symbols, previous symbols, and aliases. All these are listed in the results page. As the different names can overlap, we recommend to cross-check the identity of the selected gene.
What is the JetSet probe?
JetSet is a tool to select the optimal microarray probe set to represent a gene. More info on the JetSet homepage.
Try our other web tools as well:
The Kaplan Meier plotter is capable to assess the correlation between the expression of 30k genes (mRNA, miRNA, and protein) and survival in samples from 21 tumor types including breast, ovarian, lung, & gastric cancer. Primary purpose of the tool is a meta-analysis based discovery and validation of survival biomarkers.
The TNMplotter can is capable to compare gene expression between tumor, normal, and metastatic samples for any gene using 57 thousand samples with transcriptome-level gene expression data.
The muTarget platform is designed to connect mutation status to gene expression changes in solid tumours. It has two major functions: 1) with a "Genotype" run one can identify gene(s) showing altered expression in samples harboring a mutated input gene; 2) with a "Target" run one can identify mutations resulting in expression change in the input gene.
The multiple testing platform uses a list of user-uploaded p values to compute the five most frequently used multiple testing adjustment tools, including the Bonferroni, the Holm, the Hochberg corrections, the False Discovery Rate (FDR), and the q-value.