Cancer
Francisco García edited this page Jan 26, 2015
·
7 revisions
Pages 113
- Home
- Affymetrix
- affymetrix_expression_normalization_with_apt
- Agilent
- Association Analysis
- Association Analysis doc
- Babelomics version
- Babelomics web structure
- Burden test
- Cancer
- CDF
- Changes in this version
- Class comparison. Worked examples and exercises
- Class prediction
- Class prediction. Worked examples and exercises
- Clustering
- Clustering. Worked examples and exercises
- Cross hybridization
- data matrix expression
- Data types
- Define your comparison
- Detailed example of analysis of expression data in Babelomics: from raw data to expression differential and functional profiling
- Differential Expression for arrays
- Differential Expression for RNA Seq
- Dye bias
- Edit
- Edit your data
- example data
- Expression
- Expression array pipeline
- FAQ
- Functional
- Functional Gene Set Network Enrichment
- Functional GO Enrichment
- GAL
- Gene Set Enrichment
- Gene Set Network Enrichment (Network Miner)
- Gene vs annotation
- Genepix
- Genomics
- Genomics doc
- How to cite babelomics
- Id
- Logging in
- Main areas. Cancer
- Main areas. Expression
- Main areas. Functional
- Main areas. Genomics
- Main areas. Processing
- Main areas: Cancer
- Main areas: Expression
- Main areas: Functional
- Main areas: Genomics
- Main areas: Processing
- Network Enrichment (SNOW)
- Other biological data
- Overview and pipelines
- p values adjusted for multiple testing
- PED
- PED_MAP zipped
- Pipelines
- plink.assoc
- plink.assoc.linear
- plink.assoc.logistic
- plink.fisher
- plink.hh
- plink.log
- plink.tdt
- Preprocessing for data matrix
- Preprocessing for microarrays
- Preprocessing for RNA Seq
- Processing
- Ranked
- Requirements
- RNA Seq Normalization
- RNA Seq pipeline
- SDK (Software Development Kit)
- Single Enrichment
- Single Enrichment. Options
- SNPs array pipeline
- Software and databases used
- Technical Info
- The Babelomics Team
- tut_SNP_association
- Tutorial
- Tutorial Affymetrix Expression Microarray Normalization
- Tutorial Agilent One Color Microarray Normalization
- Tutorial Agilent Two Colors Microarray Normalization
- Tutorial Burden test
- Tutorial Class prediction
- Tutorial Clustering
- Tutorial Data matrix preprocessing
- Tutorial Differential Expression for arrays
- Tutorial Differential Expression for RNA Seq
- Tutorial Expression
- Tutorial Expression. Class comparison
- Tutorial Expression. Correlation
- Tutorial Expression. Survival
- Tutorial Functional
- Tutorial Genepix One Color Microarray Normalization
- Tutorial Genepix Two Colors Microarray Normalization
- Tutorial Genomics
- Tutorial OncodriveClust
- Tutorial OncodriveFM
- Tutorial Processing
- Tutorial SNP Association Analysis
- Tutorial SNP stratification
- Upload your data
- VCF 4.0
- VCF file pipeline
- Visualization tools
- Worked examples
- Workflow
- Show 98 more pages…
General
Tutorial
Analysis tools
Worked examples
-
Expression
-
Functional
Clone this wiki locally
Babelomics includes two approaches to analyse cancer genomic sequences:
-
[OncodriveFM](Tutorial OncodriveFM) is a method to identify cancer drivers from cancer somatic mutations in a cohort of tumors. It computes the bias towards the accumulation of variants with high functional impact (FM bias).
-
[OncodriveClust](Tutorial OncodriveClust) is a method to identify genes in which mutations accumulate within specific regions of the protein, which denote events selected by the tumour. It computes a score measuring the mutation clustering of a gene across the protein sequence and compares it with a background model.
Find the Babelomics suite at http://babelomics.org