Course on Functional Analysis. ::: Gene Set Enrichment Analysis  GSEA 


 Amos Flowers
 1 years ago
 Views:
Transcription
1 Course on Functional Analysis ::: Madrid, June 31st, Gonzalo Gómez, PhD. Bioinformatics Unit CNIO
2 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
3 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
4 ::: Introduction. GSEA MIT Broad Institute v 2.0 available since Jan 2007 v available since Feb 16th 2007 Version 2.0 includes Biocarta, Broad Institute, GeneMAPP, KEGG annotations and more... Platforms: Affymetrix, Agilent, CodeLink, custom... (Subramanian et al. PNAS )
5 ::: Introduction. ::: How works GSEA? GSEA applies KolmogorovSmirnof test to find assymmetrical distributions for defined blocks of genes in datasets whole distribution. Is this particular Gene Set enriched in my experiment? Genes selected by researcher, Biocarta pathways, GeneMAPP sets, genes sharing cytoband, genes targeted by common mirnas up to you
6 ::: Introduction. ::: KS test The Kolmogorov Smirnov test is used to determine whether two underlying onedimensional probability distributions differ, or whether an underlying probability distribution differs from a hypothesized distribution, in either case based on finite samples. The onesample KS test compares the empirical distribution function with the cumulative distribution functionspecified by the null hypothesis. The main applications are testing goodness of fit with the normal and uniform distributions. The twosample KS test is one of the most useful and general nonparametric methods for comparing two samples, as it is sensitive to differences in both location and shape of the empirical cumulative distribution functions of the two samples. Dataset distribution Gene set 1 distribution Gene set 2 distribution Number of genes Gene Expression Level
7 ::: Introduction. ClassA ClassB ::: How works GSEA? FDR< testing genes independently... ttest cutoff FDR<0.05 Biological meaning?
8 ::: Introduction. ::: How works GSEA?  ClassA ClassB Gene Set 1 Gene Set 2 Gene Set 3 Gene set 3 enriched in Class B ttest cutoff ES/NES statistic Gene set 2 enriched in Class A +
9 ::: Introduction. ES examples :::
10 ::: Introduction. The Enrichment Score ::: NES pval FDR BenjaminiHochberg
11 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
12 ::: GSEA software. Download :::
13 ::: GSEA software. Main Window :::
14 ::: GSEA software. Loading data :::!!!
15 ::: GSEA software. Running GSEA :::
16 ::: GSEA software. Leading Edge Analysis :::
17 ::: GSEA software. MSigDB ::: Chip to Chip Mapping :::
18 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
19 ::: Data Formats.
20 ::: Data Formats.
21 ::: Data Formats. Expression datasets ::: *.gct
22 ::: Data Formats. Expression datasets ::: *.res
23 ::: Data Formats. Expression datasets ::: *.pcl
24 ::: Data Formats. Expression datasets ::: *.txt
25 ::: Data Formats. Phenotype datasets ::: *.cls For categorical phenotypes (e.g. Tumor vs Control)
26 ::: Data Formats. Phenotype datasets ::: For continuous phenotypes (e.g. Gene correlated to GeneSet) Time serie (each 30 minutes) Peak profile wanted For continuous phenotypes (e.g. Gene vs Time Series)
27 ::: Data Formats. Gene Set Database ::: *.gmx
28 ::: Data Formats. Gene Set Database ::: *.gmt
29 ::: Data Formats. Other formats::: *.chip *.grp
30 ::: Data Formats. Ranked list format ::: *.rnk
31 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
32 ::: Using GSEA. Loading data :::
33 ::: Using GSEA. Loading data :::
34 ::: Using GSEA. Running GSEA :::
35 ::: Using GSEA. ::: MSigDB. gsea_home
36 ::: Using GSEA. Running GSEA ::: 1. Choose true (default) to have GSEA collapse each probe set in your expression dataset into a single gene vector, which is identified by its HUGO gene symbol. In this case, you are using HUGO gene symbols for the analysis. The gene sets that you use for the analysis must use HUGO gene symbols to identify the genes in the gene sets. 2. Choose false to use your expression dataset "as is." In this case, you are using the probe identifiers that are in your expression dataset for the analysis. The gene sets that you use for the analysis must also use these probe identifiers to identify the genes in the gene sets.
37 ::: Using GSEA. Running GSEA ::: Phenotype Gene Sets (few samples)
38 ::: Using GSEA. Running GSEA :::
39 ::: Using GSEA. Chip2Chip mapping ::: Chip2Chip translates the gene identifiers in a gene sets from HUGO gene symbols to the probe identifiers for a selected DNA chip.
40 ::: Using GSEA. Enrichment statistic ::: To calculate the enrichment score, GSEA first walks down the ranked list of genes increasing a runningsum statistic when a gene is in the gene set and decreasing it when it is not. The enrichment score is the maximum deviation from zero encountered during that walk. This parameter affects the runningsum statistic used for the analysis.
41 ::: Using GSEA. Ranking Metric ::: Signal2Noise ttest Cosine Euclidean Manhatten Pearson (time series) Ratio of Classes Diff of Classes Log2_Ratio_of_Classes Categorical phenotypes Continuous phenotypes
42 ::: Using GSEA. Ranking Metric :::
43 ::: Using GSEA. Ranking Metric :::
44 ::: Using GSEA. More parameters ::: real abs parameter to determine whether to sort the genes in descending (default) or ascending order.
45 ::: Using GSEA. Launching Analysis :::
46 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
47 ::: GSEA output. By default in gsea_home Results Accession ::: C:\Documents and settings\username\gsea_home /Users/yourhome/gsea_home
48 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
49 ::: GSEA results. Index.html ::: Heat map of the top 50 features for each phenotype and a plot showing the correlation between the ranked genes and the phenotypes. In a heat map, expression values are represented as colors, where the range of colors (red, pink, light blue, dark blue) shows the range of expression values (high, moderate, low, lowest).
50 ::: GSEA results. Enrichment results in html :::
51 ::: GSEA results. Enrichment results in html :::
52 ::: GSEA results. Enrichment results in html ::: How can I decide about my results? FDR 0.25 NOM pval 0.05
53 ::: Contents. 1. Introduction. 2. GSEA Software 3. Data Formats 4. Using GSEA 5. GSEA Output 6. GSEA Results 7. Leading Edge Analysis
54 ::: GSEA results. Leading Edge Analysis :::
55 ::: GSEA results. Leading Edge Analysis ::: HeatMap SettoSet Histogram Gene in Subsets
56 ::: GSEA results. Leading Edge Analysis ::: Heat Map The heat map shows the (clustered) genes in the leading edge subsets. In a heat map, expression values are represented as colors, where the range of colors (red, pink, light blue, dark blue) shows the range of expression values (high, moderate, low, lowest).
57 ::: GSEA results. Leading Edge Analysis ::: SettoSet The graph uses color intensity to show the overlap between subsets: the darker the color, the greater the overlap between the subsets.. When you compare a leading edge subset to itself, its members completely overlap so the corresponding cell is dark green. When you compare two subsets that have no overlapping members, the corresponding cell is white.
58 ::: GSEA results. Leading Edge Analysis ::: Gene in Subsets The graph shows each gene and the number of subsets in which it appears.
59 ::: GSEA results. Leading Edge Analysis ::: Histogram The last plot is a histogram, where the Jacquard is the intersection divided by the union for a pair of leading edge subsets. Number of Occurrences is the number of leading edge subset pairs in a particular bin. In this example, most subset pairs have no overlap (Jacquard = 0).
60 ::: GSEA & FatiScan. Detects significant functions with Gene Ontology InterPro motifs, Swissprot KW and KEGG pathways in lists of genes ordered according to differents characteristics.
61 T H A N K S
Tutorial for proteome data analysis using the Perseus software platform
Tutorial for proteome data analysis using the Perseus software platform Laboratory of Mass Spectrometry, LNBio, CNPEM Tutorial version 1.0, January 2014. Note: This tutorial was written based on the information
More informationPackage GSA. R topics documented: February 19, 2015
Package GSA February 19, 2015 Title Gene set analysis Version 1.03 Author Brad Efron and R. Tibshirani Description Gene set analysis Maintainer Rob Tibshirani Dependencies impute
More informationGene Enrichment Analysis
a Analysis of DNA Chips and Gene Networks Spring Semester, 2009 Lecture 14a: January 21, 2010 Lecturer: Ron Shamir Scribe: Roye Rozov Gene Enrichment Analysis 14.1 Introduction This lecture introduces
More informationAnalyzing microrna Data and Integrating mirna with Gene Expression Data in Partek Genomics Suite 6.6
Analyzing microrna Data and Integrating mirna with Gene Expression Data in Partek Genomics Suite 6.6 Overview This tutorial outlines how microrna data can be analyzed within Partek Genomics Suite. Additionally,
More informationTime series experiments
Time series experiments Time series experiments Why is this a separate lecture: The price of microarrays are decreasing more time series experiments are coming Often a more complex experimental design
More informationProjects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
More informationIdentification of rheumatoid arthritis and osteoarthritis patients by transcriptomebased rule set generation
Identification of rheumatoid arthritis and osterthritis patients by transcriptomebased rule set generation Bering Limited Report generated on September 19, 2014 Contents 1 Dataset summary 2 1.1 Project
More informationHierarchical Clustering Analysis
Hierarchical Clustering Analysis What is Hierarchical Clustering? Hierarchical clustering is used to group similar objects into clusters. In the beginning, each row and/or column is considered a cluster.
More informationExercise with Gene Ontology  Cytoscape  BiNGO
Exercise with Gene Ontology  Cytoscape  BiNGO This practical has material extracted from http://www.cbs.dtu.dk/chipcourse/exercises/ex_go/goexercise11.php In this exercise we will analyze microarray
More informationPackage empiricalfdr.deseq2
Type Package Package empiricalfdr.deseq2 May 27, 2015 Title SimulationBased False Discovery Rate in RNASeq Version 1.0.3 Date 20150526 Author Mikhail V. Matz Maintainer Mikhail V. Matz
More informationMethods for network visualization and gene enrichment analysis July 17, 2013. Jeremy Miller Scientist I jeremym@alleninstitute.org
Methods for network visualization and gene enrichment analysis July 17, 2013 Jeremy Miller Scientist I jeremym@alleninstitute.org Outline Visualizing networks using R Visualizing networks using outside
More informationNormality Testing in Excel
Normality Testing in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com
More informationThe data. Introducción al análisis de datos en microarrays ... Characteristics of the data: Universidad Complutense de Madrid ESCUELA DE VERANO 2007
Universidad Complutense de Madrid ESCUELA DE VERANO 2007 Universidad Complutense de Madrid ESCUELA DE VERANO 2007 Introducción al análisis de datos en microarrays 1. Introducción 2. Microarrays (tipos,
More informationAGILENT S BIOINFORMATICS ANALYSIS SOFTWARE
ACCELERATING PROGRESS IS IN OUR GENES AGILENT S BIOINFORMATICS ANALYSIS SOFTWARE GENESPRING GENE EXPRESSION (GX) MASS PROFILER PROFESSIONAL (MPP) PATHWAY ARCHITECT (PA) See Deeper. Reach Further. BIOINFORMATICS
More informationMinería de Datos ANALISIS DE UN SET DE DATOS.! Visualization Techniques! Combined Graph! Charts and Pies! Search for specific functions
Minería de Datos ANALISIS DE UN SET DE DATOS! Visualization Techniques! Combined Graph! Charts and Pies! Search for specific functions Data Mining on the DAG ü When working with large datasets, annotation
More informationProteinQuest user guide
ProteinQuest user guide 1. Introduction... 3 1.1 With ProteinQuest you can... 3 1.2 ProteinQuest basic version 4 1.3 ProteinQuest extended version... 5 2. ProteinQuest dictionaries... 6 3. Directions for
More informationHYPOTHESIS TESTING WITH SPSS:
HYPOTHESIS TESTING WITH SPSS: A NONSTATISTICIAN S GUIDE & TUTORIAL by Dr. Jim Mirabella SPSS 14.0 screenshots reprinted with permission from SPSS Inc. Published June 2006 Copyright Dr. Jim Mirabella CHAPTER
More informationt Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon
ttests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com
More informationMultiExperiment Viewer Quickstart Guide
MultiExperiment Viewer Quickstart Guide Table of Contents: I. Preface  2 II. Installing MeV  2 III. Opening a Data Set  2 IV. Filtering  6 V. Clustering a. HCL  8 b. Kmeans  11 VI. Modules a. Ttest
More informationCNV Univariate Analysis Tutorial
CNV Univariate Analysis Tutorial Release 8.1 Golden Helix, Inc. March 18, 2014 Contents 1. Overview 2 2. CNAM Optimal Segmenting 4 A. Performing CNAM Optimal Segmenting..................................
More informationNonInferiority Tests for One Mean
Chapter 45 NonInferiority ests for One Mean Introduction his module computes power and sample size for noninferiority tests in onesample designs in which the outcome is distributed as a normal random
More informationII. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
More informationTIPS FOR DOING STATISTICS IN EXCEL
TIPS FOR DOING STATISTICS IN EXCEL Before you begin, make sure that you have the DATA ANALYSIS pack running on your machine. It comes with Excel. Here s how to check if you have it, and what to do if you
More information0BComparativeMarkerSelection Documentation
0BComparativeMarkerSelection Documentation Description: Author: Computes significance values for features using several metrics, including FDR(BH), Q Value, FWER, FeatureSpecific PValue, and Bonferroni.
More informationModule 5: Statistical Analysis
Module 5: Statistical Analysis To answer more complex questions using your data, or in statistical terms, to test your hypothesis, you need to use more advanced statistical tests. This module reviews the
More informationExtracting Biological Information from Gene Lists
Extracting Biological Information from Gene Lists Simon Andrews, Laura Biggins, Boo Virk simon.andrews@babraham.ac.uk laura.biggins@babraham.ac.uk boo.virk@babraham.ac.uk v1.0 Biological material Sample
More informationBiomedicine The background. The main interest. The tools
1 Biomedicine The background The main interest? Bioinformatics Clinical informatics The tools 2 Outline 3 Outline 4 Working on Network Data Analysis HH RR Infrastructure Training BIOCOMPUTATION & STRUCTURAL
More informationAnalysis of the colorectal tumor microenvironment using integrative bioinformatic tools
MLECNIK Bernhard & BINDEA Gabriela Analysis of the colorectal tumor microenvironment using integrative bioinformatic tools INSERM U872, Jérôme Galon Team15: Integrative Cancer Immunology Cordeliers Research
More informationPackage copa. R topics documented: August 9, 2016
Package August 9, 2016 Title Functions to perform cancer outlier profile analysis. Version 1.41.0 Date 20060126 Author Maintainer COPA is a method to find genes that undergo
More informationAnalyzing the Effect of Treatment and Time on Gene Expression in Partek Genomics Suite (PGS) 6.6: A Breast Cancer Study
Analyzing the Effect of Treatment and Time on Gene Expression in Partek Genomics Suite (PGS) 6.6: A Breast Cancer Study The data for this study is taken from experiment GSE848 from the Gene Expression
More informationStepbyStep Guide to Basic Expression Analysis and Normalization
StepbyStep Guide to Basic Expression Analysis and Normalization Page 1 Introduction This document shows you how to perform a basic analysis and normalization of your data. A full review of this document
More informationLet s explore SAS Proc TTest
Let s explore SAS Proc TTest Ana Yankovsky Research Statistical Analyst Screening Programs, AHS Ana.Yankovsky@albertahealthservices.ca Goals of the presentation: 1. Look at the structure of Proc TTEST;
More informationOn testing the significance of sets of genes
On testing the significance of sets of genes Bradley Efron and Robert Tibshirani November 3, 2006 Abstract This paper discusses the problem of identifying differentially expressed groups of genes from
More informationThe microarray block. Outline. Microarray experiments. Microarray Technologies. Outline
The microarray block Bioinformatics 1317 March 006 Microarray data analysis John Gustafsson Mathematical statistics Chalmers Lectures DNA microarray technology overview (KS) of microarray data (JG) How
More informationMEASURES OF LOCATION AND SPREAD
Paper TU04 An Overview of Nonparametric Tests in SAS : When, Why, and How Paul A. Pappas and Venita DePuy Durham, North Carolina, USA ABSTRACT Most commonly used statistical procedures are based on the
More informationHypothesis testing S2
Basic medical statistics for clinical and experimental research Hypothesis testing S2 Katarzyna Jóźwiak k.jozwiak@nki.nl 2nd November 2015 1/43 Introduction Point estimation: use a sample statistic to
More informationA Streamlined Workflow for Untargeted Metabolomics
A Streamlined Workflow for Untargeted Metabolomics Employing XCMS plus, a Simultaneous Data Processing and Metabolite Identification Software Package for Rapid Untargeted Metabolite Screening Baljit K.
More informationMTH 140 Statistics Videos
MTH 140 Statistics Videos Chapter 1 Picturing Distributions with Graphs Individuals and Variables Categorical Variables: Pie Charts and Bar Graphs Categorical Variables: Pie Charts and Bar Graphs Quantitative
More informationMedical Information Management & Mining. You Chen Jan,15, 2013 You.chen@vanderbilt.edu
Medical Information Management & Mining You Chen Jan,15, 2013 You.chen@vanderbilt.edu 1 Trees Building Materials Trees cannot be used to build a house directly. How can we transform trees to building materials?
More informationChapter G08 Nonparametric Statistics
G08 Nonparametric Statistics Chapter G08 Nonparametric Statistics Contents 1 Scope of the Chapter 2 2 Background to the Problems 2 2.1 Parametric and Nonparametric Hypothesis Testing......................
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationUsing Illumina BaseSpace Apps to Analyze RNA Sequencing Data
Using Illumina BaseSpace Apps to Analyze RNA Sequencing Data The Illumina TopHat Alignment and Cufflinks Assembly and Differential Expression apps make RNA data analysis accessible to any user, regardless
More informationPackage dsstatsclient
Maintainer Author Version 4.1.0 License GPL3 Package dsstatsclient Title DataSHIELD client site stattistical functions August 20, 2015 DataSHIELD client site
More informationComparative genomic hybridization Because arrays are more than just a tool for expression analysis
Microarray Data Analysis Workshop MedVetNet Workshop, DTU 2008 Comparative genomic hybridization Because arrays are more than just a tool for expression analysis Carsten Friis ( with several slides from
More informationTesting for differences I exercises with SPSS
Testing for differences I exercises with SPSS Introduction The exercises presented here are all about the ttest and its nonparametric equivalents in their various forms. In SPSS, all these tests can
More informationTo create a histogram, you must organize the data in two columns on the worksheet. These columns must contain the following data:
You can analyze your data and display it in a histogram (a column chart that displays frequency data) by using the Histogram tool of the Analysis ToolPak. This data analysis addin is available when you
More informationIBM SPSS Statistics 20 Part 4: ChiSquare and ANOVA
CALIFORNIA STATE UNIVERSITY, LOS ANGELES INFORMATION TECHNOLOGY SERVICES IBM SPSS Statistics 20 Part 4: ChiSquare and ANOVA Summer 2013, Version 2.0 Table of Contents Introduction...2 Downloading the
More informationIntroduction to Exploratory Data Analysis
Introduction to Exploratory Data Analysis A SpaceStat Software Tutorial Copyright 2013, BioMedware, Inc. (www.biomedware.com). All rights reserved. SpaceStat and BioMedware are trademarks of BioMedware,
More informationExiqon Array Software Manual. Quick guide to data extraction from mircury LNA microrna Arrays
Exiqon Array Software Manual Quick guide to data extraction from mircury LNA microrna Arrays March 2010 Table of contents Introduction Overview...................................................... 3 ImaGene
More informationPearson's Correlation Tests
Chapter 800 Pearson's Correlation Tests Introduction The correlation coefficient, ρ (rho), is a popular statistic for describing the strength of the relationship between two variables. The correlation
More informationTutorial 5: Hypothesis Testing
Tutorial 5: Hypothesis Testing Rob Nicholls nicholls@mrclmb.cam.ac.uk MRC LMB Statistics Course 2014 Contents 1 Introduction................................ 1 2 Testing distributional assumptions....................
More informationStatistical issues in the analysis of microarray data
Statistical issues in the analysis of microarray data Daniel Gerhard Institute of Biostatistics Leibniz University of Hannover ESNATS Summerschool, Zermatt D. Gerhard (LUH) Analysis of microarray data
More informationChiSquare Test. Contingency Tables. Contingency Tables. ChiSquare Test for Independence. ChiSquare Tests for GoodnessofFit
ChiSquare Tests 15 Chapter ChiSquare Test for Independence ChiSquare Tests for Goodness Uniform Goodness Poisson Goodness Goodness Test ECDF Tests (Optional) McGrawHill/Irwin Copyright 2009 by The
More informationUnit 26: Small Sample Inference for One Mean
Unit 26: Small Sample Inference for One Mean Prerequisites Students need the background on confidence intervals and significance tests covered in Units 24 and 25. Additional Topic Coverage Additional coverage
More informationEXCEL Analysis TookPak [Statistical Analysis] 1. First of all, check to make sure that the Analysis ToolPak is installed. Here is how you do it:
EXCEL Analysis TookPak [Statistical Analysis] 1 First of all, check to make sure that the Analysis ToolPak is installed. Here is how you do it: a. From the Tools menu, choose AddIns b. Make sure Analysis
More informationWISE Power Tutorial All Exercises
ame Date Class WISE Power Tutorial All Exercises Power: The B.E.A.. Mnemonic Four interrelated features of power can be summarized using BEA B Beta Error (Power = 1 Beta Error): Beta error (or Type II
More informationWhy Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts
More informationInstitute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
More informationChapter 7 Appendix. Inference for Distributions with Excel, JMP, Minitab, SPSS, CrunchIt!, R, and TI83/84 Calculators
Chapter 7 Appendix Inference for Distributions with Excel, JMP, Minitab, SPSS, CrunchIt!, R, and TI83/84 Calculators Inference for the Mean of a Population Excel t Confidence Interval for Mean Confidence
More informationLecture 28: Chapter 11, Section 1 Categorical & Quantitative Variable Inference in Paired Design
Lecture 28: Chapter 11, Section 1 Categorical & Quantitative Variable Inference in Paired Design Inference for Relationships: 2 Approaches CatQuan Relationship: 3 Designs Inference for Paired Design Paired
More informationStatistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013
Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.11.6) Objectives
More informationStatistical Inference and ttests
1 Statistical Inference and ttests Objectives Evaluate the difference between a sample mean and a target value using a onesample ttest. Evaluate the difference between a sample mean and a target value
More informationJanuary 26, 2009 The Faculty Center for Teaching and Learning
THE BASICS OF DATA MANAGEMENT AND ANALYSIS A USER GUIDE January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS Table of Contents Table of Contents... i
More informationUser Manual. Transcriptome Analysis Console (TAC) Software. For Research Use Only. Not for use in diagnostic procedures. P/N 703150 Rev.
User Manual Transcriptome Analysis Console (TAC) Software For Research Use Only. Not for use in diagnostic procedures. P/N 703150 Rev. 1 Trademarks Affymetrix, Axiom, Command Console, DMET, GeneAtlas,
More informationNAG C Library Chapter Introduction. g08 Nonparametric Statistics
g08 Nonparametric Statistics Introduction g08 NAG C Library Chapter Introduction g08 Nonparametric Statistics Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Parametric and Nonparametric
More informationNPTEL STRUCTURAL RELIABILITY
NPTEL Course On STRUCTURAL RELIABILITY Module # 02 Lecture 6 Course Format: Web Instructor: Dr. Arunasis Chakraborty Department of Civil Engineering Indian Institute of Technology Guwahati 6. Lecture 06:
More informationData Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
More informationUNIVERSITY OF NAIROBI
UNIVERSITY OF NAIROBI MASTERS IN PROJECT PLANNING AND MANAGEMENT NAME: SARU CAROLYNN ELIZABETH REGISTRATION NO: L50/61646/2013 COURSE CODE: LDP 603 COURSE TITLE: RESEARCH METHODS LECTURER: GAKUU CHRISTOPHER
More informationChapter 2 Probability Topics SPSS T tests
Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the OneSample T test has been explained. In this handout, we also give the SPSS methods to perform
More informationSPSS Tests for Versions 9 to 13
SPSS Tests for Versions 9 to 13 Chapter 2 Descriptive Statistic (including median) Choose Analyze Descriptive statistics Frequencies... Click on variable(s) then press to move to into Variable(s): list
More informationNonparametric TwoSample Tests. Nonparametric Tests. Sign Test
Nonparametric TwoSample Tests Sign test MannWhitney Utest (a.k.a. Wilcoxon twosample test) KolmogorovSmirnov Test Wilcoxon SignedRank Test TukeyDuckworth Test 1 Nonparametric Tests Recall, nonparametric
More informationProtein Protein Interaction Networks
Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks YoungRae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 SigmaRestricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More informationSCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
More informationLecture 11 Data storage and LIMS solutions. Stéphane LE CROM lecrom@biologie.ens.fr
Lecture 11 Data storage and LIMS solutions Stéphane LE CROM lecrom@biologie.ens.fr Various steps of a DNA microarray experiment Experimental steps Data analysis Experimental design set up Chips on catalog
More informationAn introduction to IBM SPSS Statistics
An introduction to IBM SPSS Statistics Contents 1 Introduction... 1 2 Entering your data... 2 3 Preparing your data for analysis... 10 4 Exploring your data: univariate analysis... 14 5 Generating descriptive
More informationTesting Random Number Generators
Testing Random Number Generators Raj Jain Washington University Saint Louis, MO 63130 Jain@cse.wustl.edu Audio/Video recordings of this lecture are available at: http://www.cse.wustl.edu/~jain/cse57408/
More informationImproving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP
Improving the Performance of Data Mining Models with Data Preparation Using SAS Enterprise Miner Ricardo Galante, SAS Institute Brasil, São Paulo, SP ABSTRACT In data mining modelling, data preparation
More informationIBM SPSS Direct Marketing 23
IBM SPSS Direct Marketing 23 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 23, release
More informationQuality Assessment of Exon and Gene Arrays
Quality Assessment of Exon and Gene Arrays I. Introduction In this white paper we describe some quality assessment procedures that are computed from CEL files from Whole Transcript (WT) based arrays such
More informationIBM SPSS Direct Marketing 22
IBM SPSS Direct Marketing 22 Note Before using this information and the product it supports, read the information in Notices on page 25. Product Information This edition applies to version 22, release
More informationBowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition
Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology StepbyStep  Excel Microsoft Excel is a spreadsheet software application
More informationcontaining Kendall correlations; and the OUTH = option will create a data set containing Hoeffding statistics.
Getting Correlations Using PROC CORR Correlation analysis provides a method to measure the strength of a linear relationship between two numeric variables. PROC CORR can be used to compute Pearson productmoment
More informationNCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, twosample ttests, the ztest, the
More informationVisualization Quick Guide
Visualization Quick Guide A best practice guide to help you find the right visualization for your data WHAT IS DOMO? Domo is a new form of business intelligence (BI) unlike anything before an executive
More information. (3.3) n Note that supremum (3.2) must occur at one of the observed values x i or to the left of x i.
Chapter 3 KolmogorovSmirnov Tests There are many situations where experimenters need to know what is the distribution of the population of their interest. For example, if they want to use a parametric
More informationIntroduction to Statistics with GraphPad Prism (5.01) Version 1.1
Babraham Bioinformatics Introduction to Statistics with GraphPad Prism (5.01) Version 1.1 Introduction to Statistics with GraphPad Prism 2 Licence This manual is 201011, Anne SegondsPichon. This manual
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jintselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationINTERPRETING THE ONEWAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONEWAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the oneway ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
More informationThey can be obtained in HQJHQH format directly from the home page at: http://www.engene.cnb.uam.es/downloads/kobayashi.dat
HQJHQH70 *XLGHG7RXU This document contains a Guided Tour through the HQJHQH platform and it was created for training purposes with respect to the system options and analysis possibilities. It is not intended
More informationCluster software and Java TreeView
Cluster software and Java TreeView To download the software: http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm http://bonsai.hgc.jp/~mdehoon/software/cluster/manual/treeview.html Cluster 3.0
More informationGuide for Data Visualization and Analysis using ACSN
Guide for Data Visualization and Analysis using ACSN ACSN contains the NaviCell tool box, the intuitive and user friendly environment for data visualization and analysis. The tool is accessible from the
More informationChart Recipe ebook. by Mynda Treacy
Chart Recipe ebook by Mynda Treacy Knowing the best chart for your message is essential if you are to produce effective dashboard reports that clearly and succinctly convey your message. M y O n l i n
More informationMIC  Detecting Novel Associations in Large Data Sets. by Nico Güttler, Andreas Ströhlein and Matt Huska
MIC  Detecting Novel Associations in Large Data Sets by Nico Güttler, Andreas Ströhlein and Matt Huska Outline Motivation Method Results Criticism Conclusions Motivation  Goal Determine important undiscovered
More informationStepbyStep Guide to BiParental Linkage Mapping WHITE PAPER
StepbyStep Guide to BiParental Linkage Mapping WHITE PAPER JMP Genomics StepbyStep Guide to BiParental Linkage Mapping Introduction JMP Genomics offers several tools for the creation of linkage maps
More informationData Clustering. Dec 2nd, 2013 Kyrylo Bessonov
Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms kmeans Hierarchical Main
More informationBIOINF 525 Winter 2016 Foundations of Bioinformatics and Systems Biology http://tinyurl.com/bioinf525w16
Course Director: Dr. Barry Grant (DCM&B, bjgrant@med.umich.edu) Description: This is a three module course covering (1) Foundations of Bioinformatics, (2) Statistics in Bioinformatics, and (3) Systems
More informationDATA ANALYSIS. QEM Network HBCUUP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University
DATA ANALYSIS QEM Network HBCUUP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. Howard University Quantitative Research What is Statistics? Statistics (as a subject) is the science
More informationData Analysis. Using Excel. Jeffrey L. Rummel. BBA Seminar. Data in Excel. Excel Calculations of Descriptive Statistics. Single Variable Graphs
Using Excel Jeffrey L. Rummel Emory University Goizueta Business School BBA Seminar Jeffrey L. Rummel BBA Seminar 1 / 54 Excel Calculations of Descriptive Statistics Single Variable Graphs Relationships
More informationData visualization and clustering. Genomics is to no small extend a data science
Data visualization and clustering Genomics is to no small extend a data science [www.data2discovery.org] Data visualization and clustering Genomics is to no small extend a data science [Andersson et al.,
More information