Survival analyses |
Survival time definition |
Define right censored outcomes in terms of start and end dates/times and a censor date/time or indicator. |
Kaplan-Meier plots |
Plot one or more survival curves, based on categorical sample covariates or continuous-valued data, such as gene expression values. For the latter, the expression range may be flexibly split into two or more ranges (such as tertiles) to define groups.
View sample information for selected individuals (correspoding to censored tick marks, or events) from the plot, or to all individuals in a group.
Customize with color-coded line, standard errors (Peto or Greenwood), medians, numbers at risk, time scale in days, week, months or years, and plot labels. Invert the plot or convert to a log scale.
Divide patients into strata (e.g. gender-specific) for separate Kaplan-Meier analysis.
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Logrank tests |
Tests of homogeneity and trend across groups. |
Cox regression |
Multivariate analysis of survival outcome based on expression profiles and/or clinical covariates. Allows for testing of prognostic significance of genes whiule adjusting for other known prognostic factors. Stepwise Cox regression selects genes that are independently predictive of outcome. |
GeneScreen |
Screen all genes for asociation with survival outcome, using Cox regression. Optionally adjust analysis for known prognostic factors. Base selection of significant genes on a defined false discovery rate. Use a leave-n-out approach to predict outcome for cases not in the analysis data set. |
Disease classification |
2- group and k-group |
Supervised clustering:
Selection of predictive genes using univariate statistics (e.g. t-tests, ANOVA) with GeneScreen, followed by unsupervised clustering with k-mean, self-organizing maps, Bayesian classification or hierachical clustering.
Nearest shrunken centroids method for classification on a training set, an independent test set and/or a leave-n-out set.
Support Vector Machines for k-way classification and testing on an independent set.
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Metacluster |
Cumulation of results from repetitive clustering to average over many local minima, or to use a leave-n-out strategy for independently testing classification performance. |
Link to outcome |
The results of clustering or classification can be directly assessed with respect to outcome, using thumbnail Kaplan-Meier survival plots, for each cluster or class. |
Sequential experiments |
Line graphs |
Optionally, stratified by treatment or other characterisitc.
Optional standard error bars.
Find genes with similar patterns.
Display multiple genes in single plot, or as a tiled display with one gene per tile.
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Biologic integration |
General tools |
Pathways: Displays pathways (derived from KEGG, GenMAPP or custom built), with gene expression (or covariate) data superimposed. Click for information on a gene or a metabolic product. Annotated with text or thumbnail graphics that summarize key properties of the genes.
Attribute analysis: Attributes are gene classifications that include GO codes protein classification and pathway membership, in tree structure. Genes in a list are compared to all genes to determine the Attributes over-represented (with odds ratio and statistical significance).
SNPs and Genomics: Download genomic sequences, select genomic regions to display (5' promoter, exons, introns and/or 3' tail), align two or more sequences or probe sets, BLAST against full genomes, find matches to a motif, compare occurrences of a selected motif in a set of genes to a control set, highlight conserved and/or repeat genomic regions, display known SNPs.
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Cancer-specific tools |
Karyotype description is automatically interpreted to mark up a chromosome ideogram to indicate regions of loss, duplication and sites of translocation.
Loss-of-heterozygosity analysis using SNP array data, showing regions of LOH for individual samples or aggregated over a group.
Genomic amplification based on CGH data shown on the chromosome ideogram.
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Prediction |
Diagnosis/classification |
Results from classification/clustering methods, and metacluster runs can be applied to a separate set of cases to predict class. |
Outcome |
Results from multivariate Cox regressions, with optimal gene selection, can be applied to a separate set of cases to predict outcome. |
