MATLAB nuggets

1) How to initialize cell arrays

k = cell(100,1);

2) Playing with cell arrays

>> c{1} = [1,2]

c = [1x2 double]

>> c{2} = ['a']

c = [1x2 double] 'a'

>> c

c = [1x2 double] 'a'

>> c = {[1 2], ['a']}

c = [1x2 double] 'a'

>> state_mu = cell(2,1)

state_mu =

[]

>> state_mu{1} = [4 5 6]

state_mu =

[1x3 double]

[]

>> state_mu{1}(3)

ans =

v_data1 = [1 2 3 4]; v_data2 = [3 8 9]; v_data3 = [12 7 3];

v_data = {v_data1,v_data2,v_data3};

whole_virus_data1 = [v_data1 v_data2 v_data3]; % equivalent to the next statement

whole_virus_data2 = [v_data{:}]

>> state_mu = cell(5,10)

>> >> state_mu{1,3} = [1 2 3]

state_mu =

[] [] [1x3 double] [] [] [] [] [] [] []

[] [] [] [] [] [] [] [] [] []

Concatenate different cell arrays to create a single array

[state_mu{1:3,4}]

Summing cell array rows (from Loren on the art of Matlab, Summing cell rows, link)

>> state_mu{1,4} = [19 3 24 1]

>> state_mu{2,4} = [19 3 24 1]

>> >> reshape([state_mu{1:3,4}],[],2)

ans =

19 19

3 3

24 24

1 1

>> sum( reshape([state_theta{1:3,4}],[],2), 2)

ans =

>> file_name{1,:}

ans =

gendata_generate_data_1_1_19-Sep-2012.tex

ans =

gendata_generate_data_1_2_19-Sep-2012.tex

ans =

gendata_generate_data_1_3_19-Sep-2012.tex

>> {file_name{1,:}}

ans =

[1x41 char] [1x41 char] [1x41 char]

3) How to remove NaN from an array (a column vector here)

MLHdata = [1 NaN 2 3 78 NaN 86]'

MLHdata(any(isnan(MLHdata),2),:) = [10]

AND

% to specifically change a column vector which has NaNs and substitute that with some other number

% in this example, we substitute the NaNs in the 17th column of a matrix with another number

data_ptr.data(any(isnan(data_ptr.data(:,17)),2),17) = [-100]

AND

y = [ 1 13 NaN 34 NaN 45]; size(y)

y(find(isnan(y))) = []

size(y)

% array will now shrink in size (TRICK to remove NaN or other elements in array without shuffling elements around)

4) Matlab tutorial on curve fitting to ODE (by Drew Levin)

5) How to run matlab on a remote machine (courtesy Joshua Hecker)

ssh into machine

nohup matlab < myfile.m &

6) How to save a plot/figure automatically (courtesy Drew Levin)

figID = figure;

hist(state_theta1(:,4),1000)

xlabel('log_1_0 delta');

title('Histogram');

print(figID, '-djpeg', sprintf('delta_hist_%d_%s.jpg', N, date));

% OR

print(figID, '-dpdf', sprintf('test_%s.pdf', date));

% OR if you want to save current figure

saveas(gcf,'star.eps', 'psc2')

7) Display text

disp(['Simulating group ', num2str(iDoseGrp), ' ... '])

8) Call external program from Matlab (untested, taken from Matlab Newsreader forum link)

matlabpool(4);

parfor (i=1:4)

system(['H:\Mat_cod\Program\ssvv.exe', ' ', '-f', ' ', strcat('H:\Mat_cod\folder', num2str(i), '\rundata.txt')]); % Parallel run 4 applications

end

matlabpool close;

9) Getting taskid in parfor loop (from Matlab Newsreader forum link)

parfor ii = 1:100

t = getCurrentTask();

tid(ii) = t.ID;

end

10) Error handling/debugging tips (from Matlab Newsreader forum link)

"dbstop if all error" and "err.getReport()"

11) Other text importing functions

xlsread, textscan

12) Matlab command to pause execution (and wait for command/keystroke)

pause (link)

13) Matlab code to get current function name (from which it is being executed, courtesy link)

[ST,~] = dbstack();

function_name = ST.name

14) Matlab code using find

ind = find(combined(:,1) == 2.3e5);

mod_combined = combined(ind,:)

% size(mod_combined)

find(mod_combined(:,2) == 4)

15) Repeatedly add same element to array

Use repmat

a = [10; 10; 10; 10; 10;]

% add 17 to last column

[a repmat(17,size(a,1),1)]

% another example code

repmat(17, 1, 10)

> 17 17 17 17 17 17 17 17 17 17

16) Minimum of array (only non-zero elements)

% first crack; does not handle non-zero

[~, index] = min(output_array(:,5)); % 5th column has SSR

best_V0 = output_array(index,1)

% handle non-zero

[~, index] = min(output_array(find(output_array(:,5) > 0),5));

best_V0 = output_array(index,1)

16) Creating a structure array

patient.name = 'John Doe'; patient.billing = 127.00; patient.test = [79, 75, 73; 180, 178, 177.5; 220, 210, 205];

patient

patient(2).name = 'Ann Lane';

patient(2).billing = 28.50; patient(2).test = [68, 70, 68; 118, 118, 119; 172, 170, 169];

patient

17) Concatenating to a list

g_temp = [17 8 23];

temp = []

for jCount = 1:3

temp = [temp g_temp(jCount)]

end

18) Playing with cell arrays (how to rowwise mean of 2D cell array)

draw_indiv_param =

[1x3 double] [1x3 double] [1x3 double]

> draw_indiv_param{1,:}

ans =

3.9079 0.3030 -0.3871

ans =

4.0995 0.2978 -0.4733

ans =

4.0461 0.3051 -0.4551

> [draw_indiv_param{1,:}]

ans =

3.9079 0.3030 -0.3871 4.0995 0.2978 -0.4733 4.0461 0.3051 -0.4551

> iNumParam = 3;

mean(reshape([draw_indiv_param{1,:}],iNumParam,[]),2)

ans =

4.0178

0.3020

-0.4385

19) Colour in bar plots and text in X axis label (modified from link and link)

array=[ 1 2 3 ; 4 5 6 ; 7 8 9];

handle_bar = bar('v6',array);

set(handle_bar(1),'facecolor','red')

set(handle_bar(2),'facecolor',[0 1 0])

set(handle_bar(3),'facecolor','b')

set(gca,'XTickLabel',{'first', 'second', 'third'})

20) command to check where Matlab is installed

>> matlabroot

ans =

/usr/local/MATLAB/R2011a

21) command to check version

>> version

ans =

7.12.0.635 (R2011a)

22) How to convert a matlab (.m) file to MEX (on Ubuntu) Matlab version 7.12.0.635 (R2011a)

say you have a file named poc_gibbs_birds_v121allcall.m

type this at the Unix command prompt

> matlab

type this at Matlab command prompt

> mcc –m poc_gibbs_birds_v121allcall.m

type this at Matlab command prompt

> exit

just type this at command prompt

> /usr/local/MATLAB/R2011a/bin/mcc -mv poc_gibbs_birds_v121allcall.m

THEN

type this at Unix command prompt

ls -ltr

a file named run_poc_gibbs_birds_v121allcall.sh should be created

type this at Unix command prompt

./run_poc_gibbs_birds_v121allcall.sh /usr/local/MATLAB/R2011a

nohup ./run_poc_gibbs_birds_v121allcall.sh /usr/local/MATLAB/R2011a

where /usr/local/MATLAB/R2011a is the path to Matlab.

you can find this by entering matlabroot in the Matlab command prompt

23) How to suppress MATLAB warnings

Right-click on a MATLAB error. It will show options to suppress in the current file, all files, or in this line.

It will then add a comment to the end of that line.

24) Histograms with multiple colours

[nR0_P xR0_P] = hist(log10R0_P,100);

[nR0_NP xR0_NP] = hist(log10R0_NP,100);

figure

bar(xR0_P,nR0_P,'r')

hold on

bar(xR0_NP,nR0_NP,'b')

hold off

25) filter a file on some field and display minimum of some other field after filtering

filter by 7th column < 30

from this result, find minimum of 6th column

min(fid_filename( find(fid_filename(:,7) < 30) ,6))

26) Initialize array with a sequence of numbers with some step size/difference

>> [0:5:20]

ans =

0 5 10 15 20

27) Calling UNIX/shell commands from matlab

system('sed ''1d'' header.csv > noheader.csv')

28) Shell script to iterate through directories (handles directories with spaces) and compiles matlab code in each of these directories (inspired by stackoverflow)

#!/bin/bash

# Shell script to iterate through directories in pwd

for dir in */

echo $dir

# go to directory

cd "$dir"

# run mcc to compile matlab code

pwd

/usr/local/MATLAB/R2011a/bin/mcc -mv unixfile.m

# /opt/matlab/bin/mcc -mv unixfile.m

# go back to parent directory

cd ..

done

29) Shell script to compile matlab code, wait (delay) and then run compile code

#!/bin/sh

# compile code

/usr/local/MATLAB/R2011a/bin/mcc -mv unixfile.m

# delay: verify if compilation worked

sleep 10

# execute compiled code

nohup ./run_unixfile.sh /usr/local/MATLAB/R2011a

30) Replicate one number many times to create a vector

number_of_times_replicate = 100

ones(1,number_of_times_replicate) * 17

31) Use plotyy to plot on two y axes

[AX,H1,H2] = plotyy(t,V,t,zT/target,'plot')

set(get(AX(1),'Ylabel'),'String','Virus titer (log_1_0 PFU/mL)','fontsize',25,'color','black')

set(get(AX(2),'Ylabel'),'String','Target cell abundance','fontsize',25,'color','black')

set(AX(1),'ycolor','black','YLim',[-2.2 4],'YTick',[-2:1:4])

set(AX(2),'ycolor','black','YLim',[0 1],'YTick',[0:0.1:1])

% set(AX(2),'ytick',[])

% set(AX(1),'ytick',[])

set(AX(2),'fontsize',16)%,'YTick',[0:0.1:1])

set(AX(1),'fontsize',16)

% set(AX(2),'YTickLabel',[]);

% set(AX(2),'ycolor','black','YLim',[0 1],'YTick',[0:0.1:1])

set(H1,'LineStyle','-','LineWidth',2,'Color','black')

set(H2,'LineStyle','-','LineWidth',2,'Color','blue')

xlabel('Days post infection','fontsize',25);

%% get more data after computations

[AX,H1,H2] = plotyy(t,V,t,zT/target,'plot')

% hold(AX(1))

set(H1,'LineStyle','--','LineWidth',2,'Color','black')

set(H2,'LineStyle','--','LineWidth',2,'Color','blue')

set(AX(1),'ycolor','black','YLim',[-2.2 4],'YTick',[-2:1:4])

set(AX(2),'ycolor','black','YLim',[0 1],'YTick',[0:0.1:1])

set(AX(2),'fontsize',16)

%% then you will see there are multiple tick marks on right hand side Y-axis where X-axis tick marks are. I use insert text box (white colour) to paint over those extra ticks

32) Launching multiple instances of MATLAB on a Mac

Open Terminal

Type "/Applications/MATLAB_R2010bSP2.app/bin/matlab" at the command prompt

33) Basic image analysis/processing

a = imread('image_stable.tif',11); size(a)

K = mat2gray(a);

imshow(a), figure, imshow(K)

34) To convert a cell array of strings (such as might have been obtained after importdata on file that has some text columns) into numbers

use str2double

35) Write/output array to csv file

csvwrite('test.csv',array);

% write to an excel file

xlswrite('test.xls',temp_array);

% xlswrite('test.txt',temp_array) % would work also

36) Converting to a cell array of strings (mathworks)

label = [ 'P '; 'P '; 'P '; 'P '; 'P '; 'P '; 'P '; 'P '; 'P '; 'NP'; 'NP'; 'NP'; 'NP'; 'NP'; 'NP'; 'NP'; 'NP';]

species = cellstr(label)

37) Function to save all workspace variables to a .mat file

save(filename)

38) Matlab image processing function

bwmorph

31) Analyzing gene expression data using bioinformatics toolbox

DataMatrix format

Create a DataMatrix

Representing gene expression data values in DataMatrix object

Perform two-sample t-test using mattest

c = [190 100; 189 120; 145 134; 140 156]

d = [1000 1100; 1200 1145; 1300 1234; 1134 1453]

PValues = mattest(c,d)

FDR = mafdr(PValues)

mavolcanoplot(c,d, PValues)

Estimate false discovery rate using mafdr

List of functions in bioinformatics toolbox

Create a volcano plot

Note: for FDR by Bejamini-Hochberg method use

FDR = mafdr(PValues, 'BHFDR', true)

32) A practical example of using mattest (bioinformatics package)

data_ptr_healthy = importdata('combined_healthy_compound_preds.txt')

data_ptr_disease = importdata('combined_disease_compound_preds.txt')

PValues = mattest(data_ptr_healthy.data,data_ptr_disease.data,'VarType','unequal','permute',true,'showplot',true,'showhist',true)

FDR = mafdr(PValues)

mavolcanoplot(data_ptr_healthy.data, data_ptr_disease.data,PValues)

index_sign = find(FDR < 0.05)

data_ptr_healthy.textdata{index_sign}

csvwrite('list_FDR.txt',FDR)

33) Another example of mattest (t-test) but with the DataMatrix object

function basic_t_test_datamatrix()

% implements a basic t-test with data matrix objects

%% import data

data_ptr_healthy = importdata('combined_healthy_compound_predictions.txt')

data_ptr_disease = importdata('combined_disease_compound_predictions.txt')

import bioma.data.*

%temp = [1:10]

%dmo_healthy = DataMatrix(data_ptr_healthy.data,data_ptr_healthy.textdata,temp)

dmo_healthy = DataMatrix(data_ptr_healthy.data,data_ptr_healthy.textdata,[1:size(data_ptr_healthy.data,2)])

%temp_2 = [1:87]

%dmo_disease = DataMatrix(data_ptr_disease.data,data_ptr_disease.textdata,temp_2)

dmo_disease = DataMatrix(data_ptr_disease.data,data_ptr_disease.textdata,[1:size(data_ptr_disease.data,2)])

[p_value t_scores] = mattest(dmo_healthy,dmo_disease,'showplot',true,'showhist',true)

%% downstream analysis FDR, volcano plot etc

FDR = mafdr(p_value)

mavolcanoplot(data_ptr_healthy.data, data_ptr_disease.data,p_value)

% index_sign = find(FDR < 0.05)

% data_ptr_healthy.textdata{index_sign}

% %csvwrite('list_diff_expr_compounds.txt',data_ptr_healthy.textdata{index_sign})

% csvwrite('list_FDR.txt',FDR)

index_sign = find(p_value < 0.05)

data_ptr_healthy.textdata{index_sign}

%csvwrite('list_diff_expr_compounds.txt',data_ptr_healthy.textdata{index_sign})

%csvwrite('list_p_value_datamatrix.txt',PValues)

34) Code to create box plots (with notch) on disease vs healthy data

data_ptr_healthy = importdata('combined_healthy_compound_preds.txt')

data_ptr_disease = importdata('combined_disease_compound_preds.txt')

data_box_plot = [ data_ptr_healthy.data(27,:)' ; data_ptr_disease.data(27,:)']

% number of sample ids/replicates

%group = [repmat('healthy',10,1); repmat('disease',87,1)]

group = [repmat('healthy',size(data_ptr_healthy.data,2),1); repmat('disease',size(data_ptr_disease.data,2),1)]

figure;

boxplot(data_box_plot,group,'notch','on')

% title command must come AFTER boxplot command

title('Glutathione')

% On another note, if you need to do something fancy (like rotate labels etc) see this on MATLAB File Exchange

35) Hash tables/dictionaries in MATLAB (adapted from response by Amro in stackoverflow)

PRISM_predictions = importdata('list_diff_expr_compounds_PRISM_pvalues.txt');

PRISM_predictions.data

key_PRISM = PRISM_predictions.data;

values_PRISM = PRISM_predictions.textdata;

PRISM_Map = containers.Map(key_PRISM,values_PRISM);

RISK_predictions = importdata('list_diff_expr_compounds_PRISM_pvalues.txt');

RISK_predictions.data

key_RISK = RISK_predictions.data;

values_RISK = RISK_predictions.textdata;

RISK_Map = containers.Map(key_RISK,values_RISK);

36) Other string related functions

char

cellstr

37) A great resource on t-test and potential pitfalls in MATLAB (link)

38) Regular expressions

(A) Simple calse

cmp_name = 'C15678 glujatho'

>> regexpi(cmp_name,'C15679*','end')

ans =

# 'end' option returns the end index if there is a match

# so if the length of the compound name is 6 characters, then

# you want to test if regexpi with 'end' option returns 6

if regexpi(cmp_name,'C15679*','end')

disp('found')

else

disp('not found')

end

(B) A more complicated case (regexpi could return a cell array sometimes)

temp_result = regexpi(file_ptr.textdata(iInnerCount,iCompound_Name_fullKEGG_SHOTGUN),(temp_name_halfkegg_metabol),'end');

if ~isempty(temp_result{1}) && temp_result{1} == 6

disp('found')

else

disp('not found')

end

39) Paired/matched t-test

%% do a paired t-test on predicted compounds and metabolomics data

[ttest_unpaired_hvalue ttest_unpaired_pvalue] = ttest(log10(healthy_array(:,1)),log10(healthy_array(:,2)))

40) Test for normality (Jarque-Bera test)

[h_value p_value] = jbtest(x_data)

41) Wilcoxon rank-sum test

ranksum()

Example:

[temp_p_value temp_h_value temp_wilcox_stats] = ranksum( ...

file_ptr.data(iCount - iNum_metadata_rows,iExtent_RISK_healthy), ...

file_ptr.data(iCount - iNum_metadata_rows,iExtent_RISK_ibd) ...

)

% needs a vector not matrix

% more elaborate example with a for loop iterating through a matrix

data_ptr_healthy = importdata('healthy.csv');

data_ptr_disease = importdata('disease.csv');

iTempCount = 1;

for iCount = 1:size(data_ptr_healthy.data,1)

[temp_p_value temp_h_value temp_wilcox_stats] = ranksum( ...

data_ptr_healthy.data(iCount,:), ...

data_ptr_disease.data(iCount,:) ...

)

array_wilcox_risk(iTempCount,:) = [temp_p_value temp_h_value temp_wilcox_stats.ranksum]

iTempCount = iTempCount + 1;

end

42) Convert linearize a matrix into a vector

>> a = [ 1 2; 13 45; 23 -19]

a =

1 2

13 45

23 -19

>> reshape(a,1,6)

ans =

1 13 23 2 45 -19

43) Hierarchical clustering with heatmaps and dendrograms

clustergram

44) Save a matrix/variables to a tab delimited file

dlmwrite('output_tabdel.txt',out_array,'delimiter','\t')

45) gca to get current axis, see also axes and get() and set() commands

46) run unix command within matlab

unix('ls -ltr')

47) Alternative to using strcat to join strings (just make an array)

unix_command_3 = ['paste temp_' date '.txt ' temp_output_file ' >> ' final_output_file ]

unix(unix_command_3)

48) Method to find sum of the number that are significant (say FDR < 0.05)

output_fdr = mafdr(PValues);

size(find(output_fdr < 0.05),2)

% OR

output_fdr = mafdr(PValues);

iNum_signif = output_fdr < 0.05;

sum(iNum_signif)

49) Find names of files in folder/directory (inspired by a solution by Shannon Nicley Demlow) using dir command

name_files = dir;

num_files = length(name_files)

for iCount = 1:num_files

name_files(iCount).name

end

50) Save figure using saveas

saveas(gcf, 'figure.tiff', 'tiffn')

51) Generate linearly spaced vectors using linspace

linspace(1,100,10) # generate 10 points between 1 to 100

ans =

1 12 23 34 45 56 67 78 89 100

52) Solve PDEs

Use pdepe (that solves initial boundary value problems for parabolic-elliptic PDEs in 1 dimension using finite elements)

See file pdex1.m for details (edit pdex1.m)

Also can use pdetool (GUI)

53) Use format long to show numbers in long format

format long

54) Show all variables in workspace (useful when you dont have a local installation of matlab)

who

55) Save workspace variables to mat file

% save(filename)

save(sprintf('mdsine_summary_output_%s.mat',date))

56) Excellent collection of matlab functions (submission on Matlab Central)

57) MATLAB function to do LASSO (from the documentation page)

X = randn(100,5)

r = [0;2;0;-3;0] % only two nonzero coefficients

Y = X*r + randn(100,1)*.1 % small added noise

%%Construct the default lasso fit.

B = lasso(X,Y)

58) Usage of subplot command

% first two parameters are like array indices

figID = figure;

subplot(2,3,1)

hist(w_vector_array(iBurnIn+1:end,1))

hold on

subplot(2,3,2)

hist(w_vector_array(iBurnIn+1:end,2))

hold on

subplot(2,3,3)

hist(w_vector_array(iBurnIn+1:end,3))

hold on

subplot(2,3,4)

hist(w_vector_array(iBurnIn+1:end,4))

hold on

subplot(2,3,[5 6])

hist(w_vector_array(iBurnIn+1:end,5))

hold off

59) Having inline LaTeX commands in matlab code

%% do Bayesian regression

% w is vector of regressors

% P(w|D) ~ N(w| mu, lambda)

% mu = a * lambda^(-1) * A{transpose} * y

% lambda = a * A{transpose} * A + b*I

% $P(w|D) \sim N(w| \mu, \lambda)$

% $\mu = a * \lambda^{-1} * A^{T} * y$

% $\lambda = a * A^{T} * A + b*I$

60) Poisson probability density function

%poisspdf(x,lambda)

poisspdf(13,2)

lambda = 10;

poissrnd(lambda)

61) Binomial probability density function

adapted from matlab website

A Quality Assurance inspector tests 200 circuit boards a day. If 2% of the boards have defects, what is the probability that the inspector will find no defective boards on any given day?

binopdf(0,200,0.02)

ans = 0.0176

What is the most likely number of defective boards the inspector will find?

defects=0:200;

y = binopdf(defects,200,.02);

[x,i]=max(y);

defects(i)

ans = 4

rng default; % for reproducibility

r = binornd(100,0.9)

[phat, pci] = binofit(r,100)

binornd(100,0.02)

62) Persistent variables: variables that are local to the function but persistent between function calls

persistent X, Y, Z

63) Using profiler (from MATLAB documentation)

profile on

plot(magic(35))

profile viewer

p = profile('info');

profsave(p,'profile_results')

64) Different indexing schemes in MATLAB and co-distributed arrays (while working with parallel workers)

65) Using varargin and nargin to have variable number of arguments to functions (from MATLAB documentation)

% nargin stores number of arguments

function c = addme(a,b)

switch nargin

case 2

c = a + b;

case 1

c = a + a;

otherwise

c = 0;

end

% varargin stores function arguments

function varlist(varargin)

fprintf('Number of arguments: %d\n',nargin)

celldisp(varargin)

66) Tutorials on MATLAB parallel computing and GPU computing (link) and running MATLAB code on a cluster (link)

67) PCA plot in matlab

mapcaplot

68) SVM in matlab

svmtrain

svmclassify

69) Read phylogenetic tree

phytreeread

67) Machine learning with MATLAB (code) (webinar)

68) Cross-validation (machine learning) in matlab

cvpartition (create a partition)

crossvalind (generate indices) (also see SVM classifier here)

69) Pattern recognition with neural network

patternnet

Example (from mathworks documentation page)

[x,t] = iris_dataset;

net = patternnet(10);

net = train(net,x,t);

view(net)

y = net(x);

perf = perform(net,t,y);

classes = vec2ind(y);

70) Train neural network (machine learning)

train

71) Generate ROC curve (machine learning)

perfcurve

72) Confusion matrix (machine learning)

confusionmat

73) Discriminant analysis (machine learning)

classify

74) Biograph object

biograph

Example -

g = biograph(CMatrix)

view(g);

%% save visualization (from link)

gui_g = biograph.bggui(g);

f = figure;

copyobj(gui_g.biograph.hgAxes,f);

f = get(gui_g.biograph.hgAxes, 'Parent');

print(f, '-dpdf', ['network_visualization_' date '.pdf' ])

75) Draw from inverse Gaussian distribution or any other

pd = makedist('InverseGaussian','mu',mu,'lambda',lambda);

r = random(pd)

76) Basic code to simulate gamma distribution

function gamma_test()

alpha_shape = 10;%0.1

scale = 0.5

beta_rate = 1/scale

for iCount=1:1000

samples(iCount) = gamrnd(alpha_shape,beta_rate);

end

hist(samples,100)

disp('mean')

alpha_shape*scale

disp('variance')

alpha_shape*scale^2

77) Basic code to simulate negative binomial distribution

p = 0.1;

r = 10;

for iCount=1:1000

%% RND = nbinrnd(R,P) is a matrix of random numbers chosen from a negative binomial distribution

%% with corresponding number of successes, R and probability of success in a single trial

samples(iCount) = nbinrnd(r,p);

end

figID_2 = figure

hist(samples,100)

print(figID_2, '-dpdf', sprintf('nbin_testsamples_2_%s.pdf', date));

disp('mean')

p*r/(1-p)

disp('variance')

p*r/((1-p)^2)

78) Function to debug matlab programs

keyboard (link)

% can examine all workspace variables, etc

% return to continue execution, dbquit to exit

79) Check if variable has infinity

isinf()

80) Wishart distribution

Sigma = [1 .5; .5 2]; df = 10; S1 = wishrnd(Sigma,df)/df

S1 = 1.7959 0.64107 0.64107 1.5496

df = 1000; S2 = wishrnd(Sigma,df)/df

S2 = 0.9842 0.50158 0.50158 2.1682

81) Pass control to next iteration of for or while loop

continue

82) Kalman filters for object tracking

83) Parameterizing using anonymous functions (how to pass additional parameters to MATLAB functions)

84) Finding peaks in signals and time series data

% [pks,locs] = findpeaks(data)

Y = [ 1 2 1.1 3.4 5.6 1.9 0]

[pks,locs] = findpeaks(Y)

pks =

2.0000 5.6000

locs =

2 5

85) Successive differences of elements in a vector (diff)

A = [ 1 2 7 9 14]

diff(A)

ans =

1 5 2 5

86) Exception handling

try

catch

<error handling> % OR continue OR disp('error ignored')

end

87) Fast Fourier Transform (fft)

Example code

88) Bar plot with different text labels (from MATLABCentral)

fruit = [2 5 6 7]

bar(fruit)

set(gca,'XTickLabel',{'apples', 'oranges', 'strawberries', 'pears'})

89) Fitting generalized linear models (glmfit)

% Example (adapted from MATLAB documentation)

load fisheriris

X = meas(51:end,:);

y = strcmp('versicolor',species(51:end));

b = glmfit(X,y,'binomial','link','logit')

90) LASSO for generalized linear model (lassoglm)

% Also see this link

% Example (adapted from MATLAB documentation)

load fisheriris

X = meas(51:end,:);

y = strcmp('versicolor',species(51:end));

[B FitInfo] = lassoglm(X,y,'binomial','CV',10)

[B FitInfo] = lassoglm(X,y,'binomial')

lassoPlot(B,FitInfo,'PlotType','CV'); saveas(gcf,'lassplot_1.eps', 'psc2')

lassoPlot(B,FitInfo,'PlotType','Lambda','XScale','log'); saveas(gcf,'lassplot_2.eps', 'psc2')

91) Mapping functions in MATLAB (adapted from link)

% California map axes

figure; ax = usamap('california');

setm(ax, 'FFaceColor', [.5 .7 .9])

title('California map')

% read shapefile of US states with names and locations

states = geoshape(shaperead('usastatehi.shp', 'UseGeoCoords', true));

% display map

geoshow(states, 'Parent',ax)

lat = 37.773972

lon = -122.431297

linem(lat, lon, 'LineStyle','none', 'LineWidth',2, 'Color','r', ...

'Marker','x', 'MarkerSize',10)

saveas(gcf,'san_francisco.eps', 'psc2')

92) Remove duplicates and elements in another array (both arrays have a one-to-one correspondence) (using unique)

% DOES NOT WORK

% t has duplicates and V is another vector with one-to-one correspondence

temp_mat = [t; V];

a = unique(temp_mat,'rows');

t_mod = a(:,1);

V_mod = a(:,2);

% interpolate

virus_vector = interp1(t_mod, V_mod, [2 4 6],'spline')

disp('SSR WT')

% calculate SSR

sum((virus_vector' - fileptr_wt.data(:,2)).^2)

93) matlabpool replaced by paropen and delete (link for function to override)

94) Histogram colour by condition

% get metagene score and label into one matrix

p = [metagene_raw_log_score, Y==1];

% find index of responders and non-responders

idx = (p(:,2) == 1);

idx2 = (p(:,2) == 0);

% filter nad get separately metagene scores for resonders and non-responders

p_nonresponder = p(idx);

p_responder = p(idx2);

figure;

h1 = histogram(p_responder);

hold on;

h2 = histogram(p_nonresponder);

legend('IFX responder','IFX non-responder');

xlabel('Metagene score'); ylabel('Frequency'); title('Distribution of metagene score');

hold off

95) How to add matlab to PATH variable

If MATLAB is installed on ~/MATLAB/bin

then at UNIX command prompt

export PATH=$PATH:~/MATLAB/bin

96) Example to perform deep learning text classification in MATLAB (link)