Estoril Education Day
|
|
- Georgina Wilkinson
- 6 years ago
- Views:
Transcription
1 Estoril Education Day -Experimental design in Proteomics October 23rd, 2010 Peter James
2 Note Taking All the Powerpoint slides from the Talks are available for download from: protein_technology/hupo %2C_eupa_and_nordic_qp_courses/ estoril_education_day/
3 Is this Course Necessary? Journal Guidelines: Journal of Proteome Research The methods for how the biological reliability of measurements was validated using biological replicates, statistical methods, independent experiments, etc. The methods for how the analytical reliability of measurements was validated using technical replicates and statistical methods. The treatment of relevant systematic error effects such as peptides shared by multiple proteins, interference from overlapping precursor ions, incomplete isotope labeling, bias correction for pipetting error, etc. The treatment of random error issues such as outlier rejection and the categorical exclusion of data by thresholds, for example, based on signal to noise or minimum ion counts. All quantitative results upon which conclusions are based must bear proper estimates of uncertainty and the methods for the error analysis should be clearly described.
4 Is this Course Necessary? Journal Guidelines: Journal of Proteomics The experimental design must be provided and must include details of the number of biological and analytical replicates. Only one biological/analytical replicate will not be acceptable. In clinical studies, it is highly desirable that a power analysis predicting the appropriate sample size for subsequent statistical analysis of the data is carried out. For expression analysis studies, summary statistics (mean, standard deviation) must be provided and results of statistical analysis must be shown. Reporting fold differences alone is not acceptable. Authors must report the following: methods of data normalization, transformation, missing value handling, the statistical tests used, the degrees of freedom and the statistical package or program used. Where biologically important differences in protein (gene) expression are reported, confirmatory data (e.g. from Western blot, RT-PCR analysis, etc.) are desirable. For biomarker discovery/validation studies, the sensitivity and specificity of the biomarker(s) should be provided wherever possible. It is desirable that receiver operator characteristic (ROC) curves and areas under the curves are given.
5 Is this Course Necessary? Journal Guidelines: Molecular and Cellular Proteomics A thorough description of the experimental design, including the biological sample size and number of technical replicates of such samples or preparations derived thereof so that (bio)statistical methods may be used to assess independently the significance of the results presented. Studies in which the number of biological and/or technical replicates equals one, can generally not be accepted particularly if only few or a single peptide is used for quantification. In exceptional circumstances, other lines of evidence such as time or dose dependent experiments may be acceptable instead of technical replicates.
6 Is this Course Necessary? Journal Guidelines: Molecular and Cellular Proteomics The experimental design must be provided and must include details of the number of biological and analytical replicates. Only one biological/analytical replicate will not be acceptable. In clinical studies, it is highly desirable that a power analysis predicting the appropriate sample size for subsequent statistical analysis of the data is carried out. For expression analysis studies, summary statistics (mean, standard deviation) must be provided and results of statistical analysis must be shown. Reporting fold differences alone is not acceptable. Authors must report the following: methods of data normalization, transformation, missing value handling, the statistical tests used, the degrees of freedom and the statistical package or program used. Where biologically important differences in protein (gene) expression are reported, confirmatory data (e.g. from validated immunoassays) are desirable. For biomarker discovery/validation studies, the sensitivity and specificity of the biomarker(s) should be provided wherever possible. It is desirable that receiver operator characteristic curves and areas under the curves are given.
7 Talk Overview Introduction to experimental design Sources of error How many replicates, controls? Experimental design flow Pilot experiments Normal data? Parametric, non-parametric Idea of power to calculate needs Journal guidelines
8 Is all this
9 Experimental Design Experimental design definition The statistics that happens before an experiment Why think about it? Proper planning can save having to repeat entire experiment Reduces analysis time and lowers error rate and costs Reduces experimental time to a minimum Design the experiment to answer a biological question
10 Experimental Design Flow Pilot Study Variation, Cluster and Power Analysis Full Scale Experiment Publication Data Validation Bioinformatics Complete Analysis
11 Goals of Experimental Design Avoid experimental artifacts Eliminate bias Use a simultaneous control group Randomization Blinding Reduce sampling error Replication Balance Blocking
12 Experimental Artifacts Experimental artifacts a bias in a measurement produced by unintended consequences of experimental procedures e.g. using doxycycline to activate a cloned gene in a viral vector with a teto gene promoter switches on the gene, but also many other pathways. A scrambled insert must be used as a control Conduct your experiments under conditions that are as close to reality as possible to avoid artifacts Inadequate CO 2 in cell culture experiments leads to large variations in ph and hence protein expression
13 Can I Compare my Data Sets? Non-normalised Normalised Correction for dye or isotope label incorporation efficiency Swap labels e.g. replicate Cy3Cy5 or TMT126 for 131
14 Scaling Data to a Target Intensity Target Intensity (100) Exp. 1 Exp. 2 Exp. 3 Exp. 4 Exp. 5 Exp. 6 Exp. 7 TGT = Average intensity x Scaling Factor If scaling factor is < 3 fold, a comparison can be made between all experiments in the set
15 Eliminating Bias Use a control group A control group is a group of subjects left untreated for the treatment of interest but otherwise experiencing the same conditions as the treated subjects Randomization Randomization is the random assignment of treatments to units in an experimental study which breaks the association between potential confounding variables and the explanatory variables Blinding where some of the persons involved are prevented from knowing certain information that might lead to conscious or unconscious bias on their part, invalidating the results Single blind. Experimenter knows all facts, subjects do not Double blind. Neither experimenter nor subject know facts until the finish
16 Randomization Without randomization, the confounding variable differs among treatments
17 Randomization With randomization, the confounding variable does not differ among treatments
18 Balance In a balanced experiment, all treatments have equal sample size This maximizes power This makes tests more robust to violating assumptions
19 Blocking Blocking is the grouping of experimental units that have similar properties Within each block, treatments are randomly assigned to experimental treatments Randomized block design
20 Practical Questions to Consider How much variability does your system have? Understand and minimize variation How many treatments? How many controls? Comparative analysis (one experimental condition) Serial analysis design (multiple conditions) What level of significance is needed? More replicates needed for subtle changes
21 Three Sources of Variability Biological: Differences between samples - The ultimate goal of the research Technical: Sample preparation - Protocols and operator Systematic: MS analysis - Instruments, reagents, settings
22 Experimental Replicates Technical replicates from the same sample Allows an evaluation of bench effects to the overall variability Biological replicates from different samples Replicates that reproduce biological variables explored in the experiment Permit the use of formal statistical tests Also allows the interrogation of technical variability Gold standard Use of a standard protein digest to evaluate sensitivity, mass accuracy and search parameter settings Allows an estimation of systematic variation
23 Effective Studies may need many replicates Treatments Controls Average Differential Expression
24 How many Samples do I need? You should estimate the size of the three error sources The best way is to do a pilot experiment Use minimum three biological replicates Use minimum two technical replicates Check systematic errors with a gold standard Do a Power Analysis
25 Systematic Error Estimation: Reproducibility of retention time precision 5 days
26 Technical Error Estimation Coefficient of Variability CV% is a measure of variance amongst replicates Defined as the standard deviation (σ) divided by the mean multiplied by 100 Example: 5 values representing 5 replicates 230.4, 241.7, 252.9, 338.8, Mean = ; σ = 57.9; CV% = 23.29%
27 Which Statistical Test to Use? Assess the normality for each protein species Then select a parametric or non-parametric test Student s t-test assumes normality, independent sampling, and homogeneity of variance Mann-Whitney assumes independent sampling but not a normal distribution Frequency families of tests Parametric Non-parametric
28 Is my Data Normally Distributed? A q-q plot is a plot of the quantiles of the data set 1 against data set 2 A quantile is the value which divides the distribution such given proportion of observation below 50% equivalent to the median value If the two sets come from a population with the same distribution, the points should fall approximately along a 45 0 reference line Alternatively plot data If it shows a symmetrical peak about the mean and 68% of the data lies within 1 standard deviation from the mean, the data is normally distributed
29 Biological Error Estimation Does the Experiment make sense? Hierachical Clustering is an unsupervised process It finds structures in unlabelled data A cluster is a set of objects (replicates) that are similar to each other and dissimilar to other clusters Basic way of checking results Do similar biological replicates cluster? Do technical replicates cluster within biological clusters?
30 Estimation of Replicates Needed How many Replicates must I have to prove my hypothesis? You must define a null hypothesis The hypothesis is that there is no statistical difference between control and experiment at a defined confidence level Power Analysis can provide an estimate of samples needed One must define a confidence level One must balance sample size against error rate and size of effect
31 Visualising Data -Clustering
32 Hierachical Clustering Nearest Neighbor Algorithm is a bottom-up approach Starts with n nodes n is the size of the sample merge the 2 most similar nodes at each step stop when the desired number of clusters is reached.
33 Nearest Neighbour Algorithm Nearest Neighbor, Level 1, k = 8 clusters Nearest Neighbor, Level 2, k = 7 clusters Nearest Neighbor, Level 3, k = 6 clusters
34 Nearest Neighbour Algorithm Nearest Neighbor, Level 4, k = 5 clusters Nearest Neighbor, Level 5, k = 4 clusters Nearest Neighbor, Level 6, k = 3 clusters
35 Nearest Neighbour Algorithm Nearest Neighbor, Level 7, k = 2 clusters Nearest Neighbor, Level 8, k = 1 clusters Technical replicates should cluster together within biological replicates
36 Verification Orthogonal validation (Physiol Genomics 28: 24 32, 2006) Western blots, enzyme activity assays, But if you don t see a change twice is it- False positive in the first experiment? False negative in the second? Need new samples Why? measurement error does not lead to false positives rather there is a need to validate against sampling variability Carry out a Power Analysis
37 Power Analysis You must define a null hypothesis H0 There is no difference between the experiments and controls Finding no difference does not prove the null hypothesis We simply do not have evidence to reject it Lack of a significant effect does not have to signify the means are equal Perhaps an effect exists, but the data is too noisy to demonstrate it. We need to define the Power of the experiment the probability of detecting a real effect And of not making a type II error
38 Possible Experimental Outcomes Experimental result statistically significant p < threshold H0 false Statistical not significant p > threshold H0 True Biologically no change H0 True False positive Type one error (α) Correct rejection Biological change H0 False Correct acceptance False negative Type two error (β)
39 What is Power? Power is your ability to find a difference when a real difference exists The power of a study is determined by three factors: Alpha level (what is p value -how many false positives allowed) Sample size (number of experiments needed to get result) Effect size (how large is the biological effect) Separation of Means relative to error variance. How do you Calculate Power? The best freeware solution, G*Power is available at Works on Mac OSX and Windows XP/Vista
40 Power and Sample Size Power analysis can be used to estimate the sample size required for a particular study Too small an effect size and an effect may be missed Too large an effect size too expensive a study Different formulae/tables for calculating sample size are required according to experimental design
41 Power and Effect Size As the separation between two means increases the power also increases
42 Power and Effect Size As the variability about a mean decreases power also increases
43 Should I Pool my Samples? Pooling Taking same amount of protein from different samples and create pool. Assumption: Signal from pool represents mathematical average Advantage: Can increase number of samples measured Disadvantage: Intra-group biological variation is lost Option: Sub-pooling, possible to estimate biological variation Can result in irreversible loss of information Pool of all samples can be used as internal reference in DIGE, itraq, etc. Pool minimum three or maximum five samples Equal pooling of samples is essential
44 Mixing Replicate Types 3 readings on the 3 biological gives a total of 18 readings This is an example of pseudoreplication There are only really 3 different subjects Student s t-test, requires independent samples and cannot be used A test which allows for hierarchy in the data is needed such as a nested ANOVA
45 Getting Help Learn the Basics of Statistics Look up Wikipedia for a starting point Collaborate with Statisticians, Informatics groups etc, BEFORE you start Use a reliable Statistics Program such as SPSS now called PASW This has extensive on-line Tutorials
46 Thanks To the following for providing many slides Morten Krogh Michaela Scigelova Natasha Karp Marianne Sandin Fredrik Levander And many others
Designing Complex Omics Experiments
Designing Complex Omics Experiments Xiangqin Cui Section on Statistical Genetics Department of Biostatistics University of Alabama at Birmingham 6/15/2015 Some slides are from previous lectures given by
More informationGene Expression Data Analysis (I)
Gene Expression Data Analysis (I) Bing Zhang Department of Biomedical Informatics Vanderbilt University bing.zhang@vanderbilt.edu Bioinformatics tasks Biological question Experiment design Microarray experiment
More informationMicroarray Technique. Some background. M. Nath
Microarray Technique Some background M. Nath Outline Introduction Spotting Array Technique GeneChip Technique Data analysis Applications Conclusion Now Blind Guess? Functional Pathway Microarray Technique
More informationAnalysis of Microarray Data
Analysis of Microarray Data Lecture 3: Visualization and Functional Analysis George Bell, Ph.D. Senior Bioinformatics Scientist Bioinformatics and Research Computing Whitehead Institute Outline Review
More informationIntroduction to Bioinformatics. Fabian Hoti 6.10.
Introduction to Bioinformatics Fabian Hoti 6.10. Analysis of Microarray Data Introduction Different types of microarrays Experiment Design Data Normalization Feature selection/extraction Clustering Introduction
More informationCrowe Critical Appraisal Tool (CCAT) User Guide
Crowe Critical Appraisal Tool (CCAT) User Guide Version 1.4 (19 November 2013) Use with the CCAT Form version 1.4 only Michael Crowe, PhD michael.crowe@my.jcu.edu.au This work is licensed under the Creative
More informationOPTIMIZATION AND CV ESTIMATION OF A PLATE COUNT ASSAY USING JMP
OPTIMIZATION AND CV ESTIMATION OF A PLATE COUNT ASSAY USING JMP Author: Marianne Toft, Statistician, Novozymes A/S, Denmark ABSTRACT Some of our products are bacterial spores, for which the assay used
More informationEECS730: Introduction to Bioinformatics
EECS730: Introduction to Bioinformatics Lecture 14: Microarray Some slides were adapted from Dr. Luke Huan (University of Kansas), Dr. Shaojie Zhang (University of Central Florida), and Dr. Dong Xu and
More informationIntroduction to gene expression microarray data analysis
Introduction to gene expression microarray data analysis Outline Brief introduction: Technology and data. Statistical challenges in data analysis. Preprocessing data normalization and transformation. Useful
More informationCASE-STUDY- VALIDATION of PCR based methodology. Beata Surmacz-Cordle Senior Analytical Development Scientist
CASE-STUDY- VALIDATION of PCR based methodology Beata Surmacz-Cordle Senior Analytical Development Scientist UK RMP Pluripotent Stem Cell Platform Validation Workshop 2 nd June 2016 RT-qPCR assay for detection
More informationCalculating the Standard Error of Measurement
Calculating the Standard Error of Measurement If we make a series of measurements of the same clinical variable we expect those measurements to show some variability. This may be because the measurement
More informationTowards unbiased biomarker discovery
Towards unbiased biomarker discovery High-throughput molecular profiling technologies are routinely applied for biomarker discovery to make the drug discovery process more efficient and enable personalised
More informationOverview of Statistics used in QbD Throughout the Product Lifecycle
Overview of Statistics used in QbD Throughout the Product Lifecycle August 2014 The Windshire Group, LLC Comprehensive CMC Consulting Presentation format and purpose Method name What it is used for and/or
More informationLC/MS/MS Solutions for Biomarker Discovery QSTAR. Elite Hybrid LC/MS/MS System. More performance, more reliability, more answers
LC/MS/MS Solutions for Biomarker Discovery QSTAR Elite Hybrid LC/MS/MS System More performance, more reliability, more answers More is better and the QSTAR Elite LC/MS/MS system has more to offer. More
More informationExperimental Design Day 2
Experimental Design Day 2 Experiment Graphics Exploratory Data Analysis Final analytic approach Experiments with a Single Factor Example: Determine the effects of temperature on process yields Case I:
More informationQuantitative Analysis on the Public Protein Prospector Web Site. Introduction
Quantitative Analysis on the Public Protein Prospector Web Site Peter R. Baker 1, Nicholas J. Agard 2, Alma L. Burlingame 1 and Robert J. Chalkley 1 1 Mass Spectrometry Facility, Dept. of Pharmaceutical
More informationEvent-specific Method for the Quantification of Soybean CV127 Using Real-time PCR. Validation Report
Event-specific Method for the Quantification of Soybean CV127 Using Real-time PCR Validation Report 20 September 2011 Joint Research Centre Institute for Health and Consumer Protection Molecular Biology
More informationreverse transcription! RT 1! RT 2! RT 3!
Supplementary Figure! Entire workflow repeated 3 times! mirna! stock! -fold dilution series! to yield - copies! per µl in PCR reaction! reverse transcription! RT! pre-pcr! mastermix!!!! 3!!! 3! ddpcr!
More informationTroubleshooting of Real Time PCR Ameer Effat M. Elfarash
Troubleshooting of Real Time PCR Ameer Effat M. Elfarash Dept. of Genetics Fac. of Agriculture, Assiut Univ. aelfarash@aun.edu.eg What is Real-Time PCR used for? Gene expression analysis Disease diagnosis
More informationData Analysis on the ABI PRISM 7700 Sequence Detection System: Setting Baselines and Thresholds. Overview. Data Analysis Tutorial
Data Analysis on the ABI PRISM 7700 Sequence Detection System: Setting Baselines and Thresholds Overview In order for accuracy and precision to be optimal, the assay must be properly evaluated and a few
More informationHow to view Results with Scaffold. Proteomics Shared Resource
How to view Results with Scaffold Proteomics Shared Resource Starting out Download Scaffold from http://www.proteomes oftware.com/proteom e_software_prod_sca ffold_download.html Follow installation instructions
More informationTechnical Review. Real time PCR
Technical Review Real time PCR Normal PCR: Analyze with agarose gel Normal PCR vs Real time PCR Real-time PCR, also known as quantitative PCR (qpcr) or kinetic PCR Key feature: Used to amplify and simultaneously
More informationSECTION 11 ACUTE TOXICITY DATA ANALYSIS
SECTION 11 ACUTE TOXICITY DATA ANALYSIS 11.1 INTRODUCTION 11.1.1 The objective of acute toxicity tests with effluents and receiving waters is to identify discharges of toxic effluents in acutely toxic
More informationVICH Topic GL2 (Validation: Methodology) GUIDELINE ON VALIDATION OF ANALYTICAL PROCEDURES: METHODOLOGY
The European Agency for the Evaluation of Medicinal Products Veterinary Medicines Evaluation Unit CVMP/VICH/591/98-FINAL London, 10 December 1998 VICH Topic GL2 (Validation: Methodology) Step 7 Consensus
More informationProteomics And Cancer Biomarker Discovery. Dr. Zahid Khan Institute of chemical Sciences (ICS) University of Peshawar. Overview. Cancer.
Proteomics And Cancer Biomarker Discovery Dr. Zahid Khan Institute of chemical Sciences (ICS) University of Peshawar Overview Proteomics Cancer Aims Tools Data Base search Challenges Summary 1 Overview
More informationOutline. Analysis of Microarray Data. Most important design question. General experimental issues
Outline Analysis of Microarray Data Lecture 1: Experimental Design and Data Normalization Introduction to microarrays Experimental design Data normalization Other data transformation Exercises George Bell,
More informationApplied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur
Applied Multivariate Statistical Modeling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 20 MANOVA Case Study (Refer Slide Time:
More informationExploration and Analysis of DNA Microarray Data
Exploration and Analysis of DNA Microarray Data Dhammika Amaratunga Senior Research Fellow in Nonclinical Biostatistics Johnson & Johnson Pharmaceutical Research & Development Javier Cabrera Associate
More informationSystematic comparison of CRISPR/Cas9 and RNAi screens for essential genes
CORRECTION NOTICE Nat. Biotechnol. doi:10.1038/nbt. 3567 Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes David W Morgens, Richard M Deans, Amy Li & Michael C Bassik In the version
More informationThe Five Key Elements of a Successful Metabolomics Study
The Five Key Elements of a Successful Metabolomics Study Metabolomics: Completing the Biological Picture Metabolomics is offering new insights into systems biology, empowering biomarker discovery, and
More informationThe Kruskal-Wallis Test with Excel In 3 Simple Steps. Kilem L. Gwet, Ph.D.
The Kruskal-Wallis Test with Excel 2007 In 3 Simple Steps Kilem L. Gwet, Ph.D. Copyright c 2011 by Kilem Li Gwet, Ph.D. All rights reserved. Published by Advanced Analytics, LLC A single copy of this document
More informationHow to view Results with. Proteomics Shared Resource
How to view Results with Scaffold 3.0 Proteomics Shared Resource An overview This document is intended to walk you through Scaffold version 3.0. This is an introductory guide that goes over the basics
More informationPrimerdesign Ltd. High risk Human Papillomavirus. Multiplex screening kit. genesig kit. 100 tests. For general laboratory and research use only
Primerdesign Ltd High risk Human Papillomavirus Multiplex screening kit genesig kit 100 tests For general laboratory and research use only 1 Introduction to Human Papillomavirus Papillomaviruses are a
More informationAnalysis of Microarray Data
Analysis of Microarray Data Lecture 1: Experimental Design and Data Normalization George Bell, Ph.D. Senior Bioinformatics Scientist Bioinformatics and Research Computing Whitehead Institute Outline Introduction
More informationCS 5984: Application of Basic Clustering Algorithms to Find Expression Modules in Cancer
CS 5984: Application of Basic Clustering Algorithms to Find Expression Modules in Cancer T. M. Murali January 31, 2006 Innovative Application of Hierarchical Clustering A module map showing conditional
More informationBenchSmart 96. Semi-automated Pipetting Higher Accuracy, Greater Flexibility
BenchSmart 96 Semi-automated Pipetting Higher Accuracy, Greater Flexibility For scientists looking to maximize their data quality and research productivity, a new semiautomated approach to 96-well pipetting
More informationQuality Control Assessment in Genotyping Console
Quality Control Assessment in Genotyping Console Introduction Prior to the release of Genotyping Console (GTC) 2.1, quality control (QC) assessment of the SNP Array 6.0 assay was performed using the Dynamic
More informationGene Expression Analysis Superior Solutions for any Project
Gene Expression Analysis Superior Solutions for any Project Find Your Perfect Match ArrayXS Global Array-to-Go Focussed Comprehensive: detect the whole transcriptome reliably Certified: discover exceptional
More informationNetwork System Inference
Network System Inference Francis J. Doyle III University of California, Santa Barbara Douglas Lauffenburger Massachusetts Institute of Technology WTEC Systems Biology Final Workshop March 11, 2005 What
More informationMicroarray Gene Expression Analysis at CNIO
Microarray Gene Expression Analysis at CNIO Orlando Domínguez Genomics Unit Biotechnology Program, CNIO 8 May 2013 Workflow, from samples to Gene Expression data Experimental design user/gu/ubio Samples
More informationless sensitive than RNA-seq but more robust analysis pipelines expensive but quantitiatve standard but typically not high throughput
Chapter 11: Gene Expression The availability of an annotated genome sequence enables massively parallel analysis of gene expression. The expression of all genes in an organism can be measured in one experiment.
More informationMeasurement of uncertainty for Elisa Tests. University of Hasselt, Center for Statistics, Hasselt, Belgium
Appendix 58 Measurement of uncertainty for Elisa Tests Toussaint, J.F. 1*, Assam, P. 2, Caij, B. 1, Dekeyser, F. 1, Imberechts, H. 1, Knapen, K. 1, Goris N. 1, Molenberghs, G. 2, Mintiens, K. 1, De Clercq,
More informationValidating, Verifying, and Evaluating Your Test Methods: It s NOT a Regulatory Exercise!
Validating, Verifying, and Evaluating Your Test Methods: It s NOT a Regulatory Exercise! Pat Garrett, Ph.D., DABCC Renee Howell, Ph.D., MT(ASCP) SeraCare Life Sciences, Inc. AACC Annual Meeting July 29,
More informationDariusz Leszczynski & Martin L. Meltz March 15 th, 2006 ****************************************************************************************
Dariusz Leszczynski & Martin L. Meltz Rapporteurs Report WORKSHOP Application of Proteomics and Transcriptomics in EMF Research October 30 November 1, 2005 STUK - Radiation and Nuclear Safety Authority,
More informationCalculation of Spot Reliability Evaluation Scores (SRED) for DNA Microarray Data
Protocol Calculation of Spot Reliability Evaluation Scores (SRED) for DNA Microarray Data Kazuro Shimokawa, Rimantas Kodzius, Yonehiro Matsumura, and Yoshihide Hayashizaki This protocol was adapted from
More informationInherent variation in the reactions, type of enzymes used. Depends on the type of labeling and procedures, as well as the age of the labels.
332 Experimental design, analysis of variance and slide quality assessment in gene expression arrays Sorin Draghici*, Alexander Kuklin, Bruce Hoff & Soheil Shams Address BioDiscovery Inc 11150 West Olympic
More informationTips for Multiplexing Cell-Based Assays:
Tips for Multiplexing Cell-Based Assays: Plan for success Fall 2010 Click this icon to view speakers notes for each slide. 2010, Promega Corporation. Considerations for Successful Cell-Based Assays Assay
More informationSOP: SYBR Green-based real-time RT-PCR
SOP: SYBR Green-based real-time RT-PCR By Richard Yu Research fellow Centre for Marine Environmental Research and Innovative Technology (MERIT) Department of Biology and Chemistry City University of Hong
More informationDisclaimer This presentation expresses my personal views on this topic and must not be interpreted as the regulatory views or the policy of the FDA
On multiplicity problems related to multiple endpoints of controlled clinical trials Mohammad F. Huque, Ph.D. Div of Biometrics IV, Office of Biostatistics OTS, CDER/FDA JSM, Vancouver, August 2010 Disclaimer
More informationGenome Sequence Assembly
Genome Sequence Assembly Learning Goals: Introduce the field of bioinformatics Familiarize the student with performing sequence alignments Understand the assembly process in genome sequencing Introduction:
More informationBioinformatics Advice on Experimental Design
Bioinformatics Advice on Experimental Design Where do I start? Please refer to the following guide to better plan your experiments for good statistical analysis, best suited for your research needs. Statistics
More informationIPA Advanced Training Course
IPA Advanced Training Course Academia Sinica 2015 Oct Gene( 陳冠文 ) Supervisor and IPA certified analyst 1 Review for Introductory Training course Searching Building a Pathway Editing a Pathway for Publication
More informationCHAPTER 8 T Tests. A number of t tests are available, including: The One-Sample T Test The Paired-Samples Test The Independent-Samples T Test
CHAPTER 8 T Tests A number of t tests are available, including: The One-Sample T Test The Paired-Samples Test The Independent-Samples T Test 8.1. One-Sample T Test The One-Sample T Test procedure: Tests
More informationVyužití cílené proteomiky pro kontrolu falšování potravin: identifikace peptidových markerů v mase pomocí LC- Q Exactive MS/MS
Využití cílené proteomiky pro kontrolu falšování potravin: identifikace peptidových markerů v mase pomocí LC- Q Exactive MS/MS Michal Godula Ph.D. Thermo Fisher Scientific The world leader in serving science
More informationSupplementary Figure 1. (a) The qrt-pcr for lnc-2, lnc-6 and lnc-7 RNA level in DU145, 22Rv1, wild type HCT116 and HCT116 Dicer ex5 cells transfected
Supplementary Figure 1. (a) The qrt-pcr for lnc-2, lnc-6 and lnc-7 RNA level in DU145, 22Rv1, wild type HCT116 and HCT116 Dicer ex5 cells transfected with the sirna against lnc-2, lnc-6, lnc-7, and the
More informationSupplementary Fig. 1 related to Fig. 1 Clinical relevance of lncrna candidate
Supplementary Figure Legends Supplementary Fig. 1 related to Fig. 1 Clinical relevance of lncrna candidate BC041951 in gastric cancer. (A) The flow chart for selected candidate lncrnas in 660 up-regulated
More informationA2LA. R231 Specific Requirements: Threat Agent Testing Laboratory Accreditation Program. December 6, 2017
Laboratory Page 1 of 17 Laboratory December 6, 2017 2017 by A2LA All rights reserved. No part of this document may be reproduced in any form or by any means without the prior written permission of A2LA.
More informationWhole Transcriptome Analysis of Illumina RNA- Seq Data. Ryan Peters Field Application Specialist
Whole Transcriptome Analysis of Illumina RNA- Seq Data Ryan Peters Field Application Specialist Partek GS in your NGS Pipeline Your Start-to-Finish Solution for Analysis of Next Generation Sequencing Data
More informationPERFORMANCE MADE EASY REAL-TIME PCR
PERFORMANCE MADE EASY REAL-TIME PCR The MyGo Pro real-time PCR instrument provides unmatched performance in a convenient format. Novel Full Spectrum Optics deliver 120 optical channels of fluorescence
More informationBacteriophage MS2. genesig Standard Kit. Phage MS2 genome. 150 tests. Primerdesign Ltd. For general laboratory and research use only
TM Primerdesign Ltd Bacteriophage MS2 Phage MS2 genome genesig Standard Kit 150 tests For general laboratory and research use only 1 Introduction to Bacteriophage MS2 Bacteriophage MS2 is a non-enveloped,
More informationHuman Papillomavirus 16
TM Primerdesign Ltd Human Papillomavirus 16 E6 gene genesig Standard Kit 150 tests For general laboratory and research use only 1 Introduction to Human Papillomavirus 16 Papillomaviruses are a diverse
More informationTransfer of Methods Supporting Biologics and Vaccines
Transfer of Methods Supporting Biologics and Vaccines Timothy Schofield Arlenda Inc. tim.schofield@arlenda.com Presented at the BPD event on Case Studies in Tech Transfer esearch Triangle Park, NC, December
More informationMBios 478: Mass Spectrometry Applications [Dr. Wyrick] Slide #1. Lecture 25: Mass Spectrometry Applications
MBios 478: Mass Spectrometry Applications [Dr. Wyrick] Slide #1 Lecture 25: Mass Spectrometry Applications Measuring Protein Abundance o ICAT o DIGE Identifying Post-Translational Modifications Protein-protein
More informationExamination Assignments
Bioinformatics Institute of India H-109, Ground Floor, Sector-63, Noida-201307, UP. INDIA Tel.: 0120-4320801 / 02, M. 09818473366, 09810535368 Email: info@bii.in, Website: www.bii.in INDUSTRY PROGRAM IN
More informationA SIMULATION STUDY OF THE ROBUSTNESS OF THE LEAST MEDIAN OF SQUARES ESTIMATOR OF SLOPE IN A REGRESSION THROUGH THE ORIGIN MODEL
A SIMULATION STUDY OF THE ROBUSTNESS OF THE LEAST MEDIAN OF SQUARES ESTIMATOR OF SLOPE IN A REGRESSION THROUGH THE ORIGIN MODEL by THILANKA DILRUWANI PARANAGAMA B.Sc., University of Colombo, Sri Lanka,
More informationModeling Cardiac Hypertrophy: Endothelin-1 Induction with qrt-pcr Analysis
icell Cardiomyocytes Application Protocol Modeling Cardiac Hypertrophy: Endothelin-1 Induction with qrt-pcr Analysis Introduction Cardiac hypertrophy is characterized by several different cellular changes,
More informationDISCOVERY AND VALIDATION OF TARGETS AND BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMICS. September, 2011
DISCOVERY AND VALIDATION OF TARGETS AND BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMICS September, 2011 1 CAPRION PROTEOMICS Leading proteomics-based service provider - Biomarker and target discovery
More information2 Gene Technologies in Our Lives
CHAPTER 15 2 Gene Technologies in Our Lives SECTION Gene Technologies and Human Applications KEY IDEAS As you read this section, keep these questions in mind: For what purposes are genes and proteins manipulated?
More informationBarrack Road, The Nothe, Weymouth DT4 8UB E: T: +44 (0) F: +44 (0)
European Union Reference Laboratory for monitoring bacteriological and viral contamination of bivalve molluscs DETERMINING UNCERTAINTY OF MEASUREMENT FOR THE ENUMERATION OF E. COLI IN BIVALVE MOLLUSCS
More informationThermo Scientific Mass Spectrometric Immunoassay (MSIA) Pipette Tips. Next generation immunoaffinity. Robust quantitative platform
Thermo Scientific Mass Spectrometric Immunoassay (MSIA) Pipette Tips Next generation immunoaffinity Robust quantitative platform Immunoaffinity sample preparation Thermo Scientific Mass Spectrometric Immunoassay
More informationDraft agreed by Scientific Advice Working Party 5 September Adopted by CHMP for release for consultation 19 September
23 January 2014 EMA/CHMP/SAWP/757052/2013 Committee for Medicinal Products for Human Use (CHMP) Qualification Opinion of MCP-Mod as an efficient statistical methodology for model-based design and analysis
More informationGETTING READY FOR DATA COLLECTION
3 Chapter 7 Data Collection and Descriptive Statistics CHAPTER OBJECTIVES - STUDENTS SHOULD BE ABLE TO: Explain the steps in the data collection process. Construct a data collection form and code data
More informationA Comparison of AlphaLISA and TR-FRET Homogeneous Immunoassays in Serum-Containing Samples
application Note A Comparison of and Homogeneous Immunoassays in Serum-Containing Samples Authors Anuradha Prasad, PhD, Catherine Lautenschlager, PhD, Stephen Hurt, PhD, David Titus, PhD and Stéphane Parent,
More informationNew Stringent Two-Color Gene Expression Workflow Enables More Accurate and Reproducible Microarray Data
Application Note GENOMICS INFORMATICS PROTEOMICS METABOLOMICS A T C T GATCCTTC T G AAC GGAAC T AATTTC AA G AATCTGATCCTTG AACTACCTTCCAAGGTG New Stringent Two-Color Gene Expression Workflow Enables More
More informationGene Signal Estimates from Exon Arrays
Gene Signal Estimates from Exon Arrays I. Introduction: With exon arrays like the GeneChip Human Exon 1.0 ST Array, researchers can examine the transcriptional profile of an entire gene (Figure 1). Being
More informationRat α-melanocyte stimulating hormone (α-msh) ELISA Kit
Rat α-melanocyte stimulating hormone (α-msh) ELISA Kit For the quantitative determination of rat α-melanocyte stimulating hormone (α-msh) concentrations in serum, plasma, tissue homogenates. This package
More informationDengue Virus subtypes 1,2 3 and 4
TM Primerdesign Ltd Dengue Virus subtypes 1,2 3 and 4 3 Untranslated Region (3 UTR) genesig Standard Kit 150 tests For general laboratory and research use only 1 Introduction to Dengue Virus subtypes 1,2
More informationQuantitative real-time PCR data analysis with R
Bachelor in Informatics Engineering Computation Bachelor Dissertation Quantitative real-time PCR data analysis with R Author Ignacio MACHADO Supervisors Borja CALVO Iñaki INZA 2016 ii Acknowledgements
More informationStatistically Integrated Metabonomic-Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice
Statistically Integrated Metabonomic-Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice Mattias Rantalainen, Olivier Cloarec, Olaf Beckonert, I. D. Wilson, David Jackson, Robert Tonge,
More informationHarbingers of Failure: Online Appendix
Harbingers of Failure: Online Appendix Eric Anderson Northwestern University Kellogg School of Management Song Lin MIT Sloan School of Management Duncan Simester MIT Sloan School of Management Catherine
More informationCREDIT RISK MODELLING Using SAS
Basic Modelling Concepts Advance Credit Risk Model Development Scorecard Model Development Credit Risk Regulatory Guidelines 70 HOURS Practical Learning Live Online Classroom Weekends DexLab Certified
More informationThe Role of Mass Spectrometry for Developing Biotherapeutics: Regulatory Perspectives
The Role of Mass Spectrometry for Developing Biotherapeutics: Regulatory Perspectives Jun Park, Ph.D. Division of Monoclonal Antibodies Office of Biotechnology Products CDER/FDA CASSS, Applications of
More informationGLP/SC/01 Basic statistical tools for analytical chemistry (2 days)
GLP Consulting http://consultglp.com Courses on offer (See outlines below) GLP/SC/01 Basic statistical tools for analytical chemistry (2 days) GLP/SC/02 The GUM bottom-up evaluation technique of measurement
More informationrapiflex Innovation with Integrity Designed for Molecules that Matter. MALDI TOF/TOF
rapiflex Designed for Molecules that Matter. Innovation with Integrity MALDI TOF/TOF rapiflex TM The first MALDI-TOF/TOF that adapts to your needs. The rapiflex is the most advanced MALDI TOF/TOF system
More informationM. tuberculosis_mpb64/is611. genesig Advanced Kit. 150 tests. Primerdesign Ltd. For general laboratory and research use only
TM Primerdesign Ltd M. tuberculosis_mpb64/is611 0 genesig Advanced Kit 150 tests For general laboratory and research use only 1 Introduction to M.tuberculosis_MPB64/IS6110 2 Specificity MAX MIN The Primerdesign
More informationQuantitative Real Time PCR USING SYBR GREEN
Quantitative Real Time PCR USING SYBR GREEN SYBR Green SYBR Green is a cyanine dye that binds to double stranded DNA. When it is bound to D.S. DNA it has a much greater fluorescence than when bound to
More informationTony Mire-Sluis Vice President, Corporate, Product and Device Quality Amgen Inc
The Regulatory Implications of the ever increasing power of Mass Spectrometry and its role in the Analysis of Biotechnology Products Where do we draw the line? Tony Mire-Sluis Vice President, Corporate,
More informationMIAPE: Mass Spectrometry Informatics
MIAPE: Mass Spectrometry Informatics Pierre-Alain Binz[1,2]*, Robert Barkovich[3], Ronald C. Beavis[4], David Creasy[5], David M. Horn[6], Randall K. Julian Jr.[7], Sean L. Seymour[8], Chris F. Taylor[9],
More informationReal-Time PCR Workshop Gene Expression. Applications Absolute and Relative Quantitation
Real-Time PCR Workshop Gene Expression Applications Absolute and Relative Quantitation Absolute Quantitation Easy to understand the data, difficult to develop/qualify the standards Relative Quantitation
More informationqpcr Quantitative PCR or Real-time PCR Gives a measurement of PCR product at end of each cycle real time
qpcr qpcr Quantitative PCR or Real-time PCR Gives a measurement of PCR product at end of each cycle real time Differs from endpoint PCR gel on last cycle Used to determines relative amount of template
More informationSession 2 summary Designs & Methods. Pairwise comparisons approach. Dose finding approaches discussed: Guiding principles for good dose selection
Session 2 summary Designs & Methods Pairwise comparisons approach Dose finding approaches discussed: PK/PD Modeling (Adaptive) MCPMod Model Averaging Bayesian Adaptive Dose Ranging Emax Dose Response Model
More informationANALYSING QUANTITATIVE DATA
9 ANALYSING QUANTITATIVE DATA Although, of course, there are other software packages that can be used for quantitative data analysis, including Microsoft Excel, SPSS is perhaps the one most commonly subscribed
More informationLifecycle Management of Process Analytical Technology Procedures
Lifecycle Management of Process Analytical Technology Procedures IFPAC 2015 Marta Lichtig Senior Scientist in New Testing Technologies, ACS Member Contents General Comparison : PV guide to NIR model development
More informationRoche Molecular Biochemicals Technical Note No. LC 10/2000
Roche Molecular Biochemicals Technical Note No. LC 10/2000 LightCycler Overview of LightCycler Quantification Methods 1. General Introduction Introduction Content Definitions This Technical Note will introduce
More informationHuman Papillomavirus 52 and 52b
Techne qpcr test Human Papillomavirus 52 and 52b E6 gene 150 tests For general laboratory and research use only 1 Introduction to Human Papillomavirus 52 and 52b Papillomaviruses are a diverse group of
More informationImproved Chemistry for NGS Library Cleanup and Size Selection Speakers: Charles Cowles, PhD & Curtis Knox
Improved Chemistry for NGS Library Cleanup and Size Selection Speakers: Charles Cowles, PhD & Curtis Knox Promega Corporation Agenda What is size-selective purification and how is it used? Why is there
More informationTECHNICAL GUIDANCE MANUAL FOR HYDROGEOLOGIC INVESTIGATIONS AND GROUND WATER MONITORING CHAPTER 13 STATISTICS FOR GROUND WATER QUALITY COMPARISON
TECHNICAL GUIDANCE MANUAL FOR HYDROGEOLOGIC INVESTIGATIONS AND GROUND WATER MONITORING CHAPTER 13 STATISTICS FOR GROUND WATER QUALITY COMPARISON February 1995 TABLE OF CONTENTS BASIC STATISTICAL ASSUMPTIONS...13-1
More informationToday. Last time. Lecture 5: Discrimination (cont) Jane Fridlyand. Oct 13, 2005
Biological question Experimental design Microarray experiment Failed Lecture : Discrimination (cont) Quality Measurement Image analysis Preprocessing Jane Fridlyand Pass Normalization Sample/Condition
More informationXevo G2-S QTof and TransOmics: A Multi-Omics System for the Differential LC/MS Analysis of Proteins, Metabolites, and Lipids
Xevo G2-S QTof and TransOmics: A Multi-Omics System for the Differential LC/MS Analysis of Proteins, Metabolites, and Lipids Ian Edwards, Jayne Kirk, and Joanne Williams Waters Corporation, Manchester,
More informationEpstein Barr Virus (Human Herpes virus 4)
TM Primerdesign Ltd Epstein Barr Virus (Human Herpes virus 4) nonglycosylated membrane protein (BNRF1) gene genesig Standard Kit 150 tests For general laboratory and research use only 1 Introduction to
More information