Temple University MASS SPECTROMETRY MATLAB SIMULATION Ilyana Mushaeva and Amber Moscato Department of Electrical and Computer Engineering Temple University.

Slides:

Advertisements

Similar presentations

1 Chapter 13 Curve Fitting and Correlation This chapter will be concerned primarily with two separate but closely interrelated processes: (1) the fitting.

Advertisements

16.4 Estimating the Cost of Operations Project GuidePrepared By Dr. T. Y. LinVinayan Verenkar Computer Science Dept San Jose State University.

電腦視覺 Computer and Robot Vision I

COMPUTING INTEREST. INTEREST COST IS A MAJOR EXPENSE VARIES WITH INTEREST RATE VARIES WITH THE METHOD USED TO CALCULATE INTEREST.

Math 35 Fall Term Professor Carl Scarbnick Topic: Least Common Multiples.

Computer Vision – Image Representation (Histograms)

A New Force-Directed Graph Drawing Method Based on Edge- Edge Repulsion Chun-Cheng Lin and Hsu-Chen Yen Department of Electrical Engineering, National.

Probability Grid: A Location Estimation Scheme for Wireless Sensor Networks Presented by cychen Date ： 3/7 In Secon (Sensor and Ad Hoc Communications and.

Orthogonality and Least Squares

1 Chapter 17: Introduction to Regression. 2 Introduction to Linear Regression The Pearson correlation measures the degree to which a set of data points.

Math: Pre-algebra & Algebra

Copyright © Cengage Learning. All rights reserved. 7.6 The Inverse of a Square Matrix.

Least Common Multiple Objective: Finding Least Common Multiple (LCM)

4. Mass Spectrometry Objectives:

Example of Simple and Multiple Regression

Lecture 3: Bivariate Data & Linear Regression 1.Introduction 2.Bivariate Data 3.Linear Analysis of Data a)Freehand Linear Fit b)Least Squares Fit c)Interpolation/Extrapolation.

© 2005 Baylor University Slide 1 Fundamentals of Engineering Analysis EGR 1302 Unit 1, Lecture F Approximate Running Time - 24 minutes Distance Learning.

Introduction Tables and graphs can be represented by equations. Data represented in a table can either be analyzed as a pattern, like the data presented.

Eigenvectors and Eigenvalues

Biostatistics Unit 9 – Regression and Correlation.

PROTEIN STRUCTURE NAME: ANUSHA. INTRODUCTION Frederick Sanger was awarded his first Nobel Prize for determining the amino acid sequence of insulin, the.

Simultaneous Equations By Dr J.P.M. Whitty

MATLAB for Engineers 4E, by Holly Moore. © 2014 Pearson Education, Inc., Upper Saddle River, NJ. All rights reserved. This material is protected by Copyright.

1 Template Matching Longin Jan Latecki Temple University CIS 601 Based on a project by Roland Miezianko.

Statistics and Linear Algebra (the real thing). Vector A vector is a rectangular arrangement of number in several rows and one column. A vector is denoted.

GENERATING Scatterplots and Correlations USING Ti84.

Ch.2 Measurements and Calculations

CMPS 1371 Introduction to Computing for Engineers MATRICES.

© 2005 Baylor University Slide 1 Fundamentals of Engineering Analysis EGR Adjoint Matrix and Inverse Solutions, Cramer’s Rule.

Chapter 4 Review: Manipulating Matrices Introduction to MATLAB 7 Engineering 161.

Laxman Yetukuri T : Modeling of Proteomics Data

Temple University MASS SPECTROMETRY SOFTWARE DESIGN Ilyana Mushaeva and Amber Moscato Department of Electrical and Computer Engineering Temple University.

$100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200.

MASS SPECTROMETRY. CONTENTS Prior knowledge Background information The basic parts of a mass spectrometer The four stages of obtaining a spectrum How.

Temple University MASS SPECTROMETRY FURTHER INVESTIGATIONS Ilyana Mushaeva and Amber Moscato Department of Electrical and Computer Engineering Temple University.

INF380 - Proteomics-101 INF380 – Proteomics Chapter 10 – Spectral Comparison Spectral comparison means that an experimental spectrum is compared to theoretical.

Sequence Alignment Csc 487/687 Computing for bioinformatics.

MS Calibration for Protein Profiles We need calibration for –Accurate mass value Mass error: (Measured Mass – Theoretical Mass) X 10 6 ppm Theoretical.

Temple University MASS SPECTROMETRY FURTHER INVESTIGATIONS Ilyana Mushaeva and Amber Moscato Department of Electrical and Computer Engineering Temple University.

Temple University MASS SPECTROMETRY INTRODUCTION Ilyana Mushaeva and Amber Moscato Department of Electrical and Computer Engineering Temple University.

INF380 - Proteomics-51 INF380 – Proteomics Chapter 5 – Fundamentals of Mass Spectrometry Mass spectrometry (MS) is used for measuring the mass-to-charge.

11/23/2015Slide 1 Using a combination of tables and plots from SPSS plus spreadsheets from Excel, we will show the linkage between correlation and linear.

Software Project MassAnalyst Roeland Luitwieler Marnix Kammer April 24, 2006.

Working with Arrays in MATLAB

Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Introduction to MATLAB 7 for Engineers William J. Palm.

1 ECE 1304 Introduction to Electrical and Computer Engineering Section 1.7 Linear Algebra with MATLAB.

1 Computational Vision CSCI 363, Fall 2012 Lecture 6 Edge Detection.

MT411 Robotic Engineering Asian Institution of Technology (AIT) Chapter 1 Introduction to Matrix Narong Aphiratsakun, D.Eng.

CMPS 1371 Introduction to Computing for Engineers VECTORS.

MC 13.3 Spectroscopy, Pt III 1 Introduction to Mass Spectrometry (cont) Principles of Electron-Impact Mass Spectrometry:  A mass spectrometer produces.

What is a ZERO of a function? Morgan White Airline High School Algebra I Students, you should move on to the next slide in order to learn an introduction.

Measurements and Data. Topics Types of Data Distance Measurement Data Transformation Forms of Data Data Quality.

Notes Over 4.2 Finding the Product of Two Matrices Find the product. If it is not defined, state the reason. To multiply matrices, the number of columns.

$100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300 $400 $500 $100 $200 $300.

Regression and Correlation

ECE 1304 Introduction to Electrical and Computer Engineering

Additional data file 5A.

MassMatrix Search Results Explained

Starter Complete the quiz and hand in..

Goal: use counters to add integers

General Overview of the module and the methods

NoDupe algorithm to detect and group similar mass spectra.

Matlab Training Session 2: Matrix Operations and Relational Operators

Graphing Linear Equations

Working with Arrays in MATLAB

Chapter Thirteen McGraw-Hill/Irwin

Parallel Feature Identification and Elimination from a CFD Dataset

Presentation transcript:

Temple University MASS SPECTROMETRY MATLAB SIMULATION Ilyana Mushaeva and Amber Moscato Department of Electrical and Computer Engineering Temple University

Temple University: Slide 1 Cross Correlation Issues We created the database using matrices, where column 1 is the mass to charge ratio and column 2 is the intensity percentage. Cross correlation (xcorr2) correlates the entire matrix, and we needed it to correlate specific peaks. The results were showing us multiple peaks, even though we are only comparing five. Our variables are discrete in this case, so xcorr functions are unnecessary. Discovered this method will not work (with this simple simulation), and attempted a different approach. The Resulting Spectrum using this method:

Temple University: Slide 2 A Possible Resolution? First, we created an experimental spectrum and a database of theoretical spectra. Use a distance formula to measure distances between each peak of experimental and database spectra. The experimental spectrum is compared with a theoretical spectrum by finding the closest distance between the (x,y) pairs, adding the total distance, and comparing, at the end, which gives the least distance overall.

Temple University: Slide 3 The Protein Database This one is a perfect match. Distance of zero!

Temple University: Slide 4 Results Using Distance Method As each spectrum is compared to the experimental, the total distance is calculated by eliminating the pairs and incrementing the distance counter by that value. The program finds the shortest distance between x and o. The pdist2 function calculates that distance. Eliminate the i (experimental) and j (database) variables. Construct a new matrix with the pairs remaining, and the process continues for the length of matrix (peaks).

Temple University: Slide 5 Issues and Concerns with Approach The smallest distance is selected by both the mass to charge ratio and intensity. However, the range on each axis is different. Two points close in m/z value may have an extremely different intensity. Does this matter? If we create a database with any number of peaks, will the process still work? In each case, we only have 5 peaks, but what if the experimental spectrum has 10 and the database spectra have a range from 10 to 100?

Temple University: Slide 6 Brief Bibliography Pascal Wallisch, M. Lusignan, M. Benayoun, T.I. Baker, A.S. Dickey, and N.G. Hatsopoulos. MATLAB for Neuroscientists: An Introduction to Scientific Computing in MATLAB. Elsevier Inc The Math Works. Available at: