Yuval Hart, Weizmann 2010© 1

Introduction to Matlab & Data Analysis

Final Project: That’s all, Folks!

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Outline

Parsing files Efficient programming - vectorization Correlation coefficients Passing extra parameters Image plotting Curve Fitting & Optimization Figure handling

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“Rotation in 60 minutes”

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Rotation in 60 minutes:

During the past month you’ve measured promoter activity of 20 genes.

Your PI wants you to present your results at the next group meeting.

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To Do List

Get the sequences of the genes from a GenBank+Fasta files and calculate GC content

Display all correlation coefficients of the measured PA and relation to GC content

Find for the highest 4 genes, how correlation decays with distance from initial gene in the pathway

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To Do List

Get the sequences of the genes from a GenBank+Fasta files and calculate GC content

Display all correlation coefficients of the measured PA and relation to GC content

Find for the highest 4 genes, how correlation decays with distance from initial gene in the pathway

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GenBank file format

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Step 3: Attach every gene name with its DNA sequence

Build the structure with all needed fields:

% Build the structure Genes with the desired genes and their data: % name, startPosition, endPosition, sequence, complement (1/0), GCcontent% This is also the way to preallocate for structures:% Genes(1,sum(indGeneList))=struct( 'name', [], 'complement', [], 'sequence',[],...% 'StartPosition',[],'EndPosition',[],'GCcontent',1);

Genes=struct('name',geneNames(indGeneList),…'complement', num2cell(indComplement(indGeneList)'),... 'StartPosition',CDSpositionStartEndCelled(indGeneList,1)',…'EndPosition',CDSpositionStartEndCelled(indGeneList,2)',...'sequence',seq,'GCcontent',GCcontent);a=Genes;Note: Structures are assigned one by one only with

cell arrays

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To Do List

Get the sequences of the genes from a GenBank+Fasta files and calculate GC content

Display all correlation coefficients of the measured PA and relation to GC content

Find for the highest 4 genes, how correlation decays with distance from initial gene in the pathway

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Calculate and plot Correlation Matrix

Load the list of genes and measurements% Input:% measurement mat file contains:% geneList - a cell array of the genes Names% measurements - a matrix of 20 genes measurements at 1001 time points% GenesGCcontent - a vector of the genes GCcontent values

%measurements has a row for each gene containing its measurements through%1001 time points and the geneList namesload measurements

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Plot GC content and mean PA dependence

Plot fit results upon the previous graph:

Note: Smoothed data can lower the effect of outliers

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Calculate and plot Correlation Matrix

Calculate and display the corr. matrix

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To Do List

Get the sequences of the genes from a GenBank+Fasta files and calculate GC content

Display all correlation coefficients of the measured PA and relation to GC content

Find for the highest 4 genes, how correlation decays with distance from initial gene in the pathway

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Step 2: Fit correlations to the desired function

Using anonymous function to add more Parameters and fitting using lsqcurvefit:

function y_hat=FittingCurveExpGuess(c,x,init)% This assumes an exponential decreasing curvey_hat=init+c(1)*exp(c(2).*x);

initDis=-0.1;c0=[.7 0.1]; %assigning the initial values for the fit searchparamfunc = @(c,x)FittingCurveExpGuess(c,x,initDis); %def. of the anonymous functionExpParam=lsqcurvefit(paramfunc,c0,XdataPoints,correl,[0 -1],[1 1],options);

Function name

Initial guess

X data

Y data

Lower bound

upper bound

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Step 3: Plot the correlation data and fit

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Best of Luck in the Group Meeting !

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Best of Luck in the Group Meeting !

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This is the end, my friend, the end

"Louis, I think this is the beginning of a beautiful friendship."