Exercise 14-15/12/15. Install required packages R Packages & Data – Package Installer – Repository: CRAN / BioConductor Packages you need: – igraph (CRAN)

Slides:

Advertisements

Similar presentations

Active Reading: “Scientific Processes”

Advertisements

What is Chi-Square? Used to examine differences in the distributions of nominal data A mathematical comparison between expected frequencies and observed.

Categorical Data Analysis

Pitfalls of Hypothesis Testing + Sample Size Calculations.

Somatic Mutation Distributions Determining Cut-offs Supp. Figure 5.

Experimental design Based on Chapter 2 of D. Heath (1995). An Introduction to Experimental Design and Statistics for Biology. CRC Press.

Simultaneous Identification of Multiple Driver Pathways in Cancer Mark D. M. Leiserson, et.al.

CHI-SQUARE TEST OF INDEPENDENCE

Stat 512 – Lecture 12 Two sample comparisons (Ch. 7) Experiments revisited.

Clinical Trials, TCGA: Deep Integrative Research RT, Imaging, Pathology, “omics” Joel Saltz MD, PhD Director Center for Comprehensive Informatics.

1 © Lecture note 3 Hypothesis Testing MAKE HYPOTHESIS ©

ANOVA Greg C Elvers.

PROBABILITY & STATISTICAL INFERENCE LECTURE 6 MSc in Computing (Data Analytics)

EGR 252 S09 Ch.10 Part 3 Slide 1 Statistical Hypothesis Testing - Part 3  A statistical hypothesis is an assertion concerning one or more populations.

Back to basics – Probability, Conditional Probability and Independence Probability of an outcome in an experiment is the proportion of times that.

Statistical Methods for Rare Variant Association Test Using Summarized Data Qunyuan Zhang Ingrid Borecki, Michael A. Province Division of Statistical Genomics.

EGR 252 S10 JMB Ch.10 Part 3 Slide 1 Statistical Hypothesis Testing - Part 3  A statistical hypothesis is an assertion concerning one or more populations.

Chapter 11 Inferences about population proportions using the z statistic.

Chapter 8 Hypothesis Testing I. Significant Differences  Hypothesis testing is designed to detect significant differences: differences that did not occur.

Introduction to data analysis: Case studies with iSIKHNAS data Day 1 1/ 69.

Hypothesis Testing Errors. Hypothesis Testing Suppose we believe the average systolic blood pressure of healthy adults is normally distributed with mean.

De novo discovery of mutated driver pathways in cancer Discussion leader: Matthew Bernstein Scribe: Kun-Chieh Wang Computational Network Biology BMI 826/Computer.

Chapter 10 Statistical Inference for Two Samples More than one but less than three! Chapter 10B < X

Hypothesis test flow chart frequency data Measurement scale number of variables 1 basic χ 2 test (19.5) Table I χ 2 test for independence (19.9) Table.

Pan-cancer analysis of prognostic genes Jordan Anaya Omnes Res, In this study I have used publicly available clinical and.

CBioPortal Web resource for exploring, visualizing, and analyzing multidimentional cancer genomics data.

Creating a Marketing Plan Module 1: Competing in the Marketplace.

DICE!  You are going to make your own and then we are going to test them (later) to see if they are fair!

Module 4: How do unrealistic expectations confound the results of our analyses Case Studies in Bioinformatics Giovanni Ciriello

Supplementary Figure 1A Supplementary Figure 1A. Highlighting mutation calls that arise within two specific regions of the NFE2L2 gene from the TCGA UCEC.

Chi-Square Goodness of Fit Test. In general, the chi-square test statistic is of the form If the computed test statistic is large, then the observed and.

Statistical Fundamentals: Using Microsoft Excel for Univariate and Bivariate Analysis Alfred P. Rovai Chi-Square Goodness-of- Fit Test PowerPoint Prepared.

CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer Max Leiserson *, Hsin-Ta Wu *, Fabio Vandin, Benjamin.

Gene Set Analysis using R and Bioconductor Daniel Gusenleitner

Creating a data set From paper surveys to excel. STEPS 1.Order your filled questionnaires 2.Number your questionnaires 3.Name your variables. 4.Create.

Lesson Runs Test for Randomness. Objectives Perform a runs test for randomness Runs tests are used to test whether it is reasonable to conclude.

QNT 351 Genius Education Expert/qnt351geniusexpert.com FOR MORE CLASSES VISIT www. qnt351genius.com.

Chapter 11: Test for Comparing Group Means: Part I.

Dr. Marco Matos JOURNAL CLUB GCUH 4/07/14.

Development of a Pan-Cancer 15 Gene Expression Signature to Detect a Subgroup Driven by MAPK Signalling Nuala McCabe,1,2 Laura A. Knight, 2 Andrena McCavigan,2.

Charles M. Rudin, MD, PhD, Alexander Drilon, MD, J.T. Poirier, PhD

Engineering Statistics 2008 Spring

Optimizing Biological Data Integration

GO : the Gene Ontology & Functional enrichment analysis

Sensitivity Analysis of the MGMT-STP27 Model and Impact of Genetic and Epigenetic Context to Predict the MGMT Methylation Status in Gliomas and Other.

Applied Statistical Analysis

QNT 351 Education for Service/tutorialrank.com

The cBio Cancer Genomics Portal.

Sensitivity Analysis of the MGMT-STP27 Model and Impact of Genetic and Epigenetic Context to Predict the MGMT Methylation Status in Gliomas and Other.

AP Statistics Multiple Choice Investigation

Volume 16, Issue 10, Pages (September 2016)

Mutual exclusivity analysis identifies oncogenic

ML criticality in clinical outcome across cancer types.

Volume 32, Issue 2, Pages e6 (August 2017)

Frequency of JAK1 and JAK2 alterations and their association with overall survival in TCGA datasets. Frequency of JAK1 and JAK2 alterations and their association.

Hotspot mutations drive clustering of tumor types

Research Question: state it

FOXO4 expression is associated with the occurrence and outcome of seizures: An RNA-sequencing analysis of low-grade gliomas Yongheng Wang, Kai Tang,

Genomic alterations in non–small cell lung cancer, breast cancer, and colorectal cancer. Genomic alterations in non–small cell lung cancer, breast cancer,

8.1 The Binomial Distribution

Pan-Cancer Landscape of Aberrant DNA Methylation across Human Tumors

Charles M. Rudin, MD, PhD, Alexander Drilon, MD, J.T. Poirier, PhD

Statistical Process Control

Statistical Process Control

Gene expression patterns of SLC and ABC transporters in normal and tumor tissues. Gene expression patterns of SLC and ABC transporters in normal and tumor.

lncRNA HOXA11-AS is overexpressed in gastric cancer tissues.

Superenhancers near the KLF5 gene are focally amplified in diverse cancer types. Superenhancers near the KLF5 gene are focally amplified in diverse cancer.

A variety of ROS1 gene fusions occur across multiple tumor types.

CASP8 mutations are associated with fewer CNAs and RAS family mutations. CASP8 mutations are associated with fewer CNAs and RAS family mutations. A, number.

Presentation transcript:

Exercise 14-15/12/15

Install required packages R Packages & Data – Package Installer – Repository: CRAN / BioConductor Packages you need: – igraph (CRAN) – foreach (CRAN) – BiRewire (BioConductor) – reshape (CRAN) Install dependencies

TCGA GBM: A Case Study

Result Summary > summary(results) SetModulemoduleSizeobservedAlteredexpectedAlteredpValpAdjVal 1CDKN2A,CDK4,RB PIK3CA,PTEN,PIK3R Set:numeric ID of your module Module:your module moduleSize:number of genes in the module observedAltered: number of samples with alterations in at least one of the genes of the module, in the REAL data (observed data) expectedAltered:average number of samples with alterations in at least one of the genes across 1000 random dataset pVal:p-value pAdjVal:p-value adjusted for multiple hypotheses

TCGA studies UCEC (Endometrial cancer) – ature12113.html ature12113.html CRC (Colorectal cancer) – ature11252.html ature11252.html STAD (Stomach adenocarcinoma) – ature13480.html ature13480.html LGG (Low grade glioma) –

Final Report 1.What are the distribution of alterations of your dataset? 1.Compare the observed distributions with the random ones (model with no constraints) 2.What can you conclude from the comparison? 2.Starting from the results presented in the paper and the biological function of the genes altered in the analyzed cancer type, define the modules to test for mutual exclusivity. 1.Motivate your choice 3.Test your modules using the three null models. 1.How do the results change? 2.Why do they (or do they not) change?