used newlines in readme for clarity, update our references to the ../../../data/

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diff --git a/src/matlab/multifasta2otu/multi_gg_final.m b/src/matlab/multifasta2otu/multi_gg_final.m
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--- a/src/matlab/multifasta2otu/multi_gg_final.m
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@@ -1,210 +0,0 @@
-%This is an example of how to run Multifasta Quikr with a custom 

-%training database (in this case Greengenes OTU's within 94% identity)

-

-%make sure Matlab/Octave is in your path

-%cd /path/to/Quikr

-

-%User-defined variables

-input_directory='../separated_samples'; %path to input directory of samples

-output_directory='quikr_results'; %path to where want output files to go

-otu_table_name='gg1194_otu_octave.txt'; %name of output otu_table filename

-trainingdatabasefilename='gg_94_otus_4feb2011.fasta'; %full path to the FASTA file you wish to use as a training database

-

-

-mkdir([output_directory]);

-thedirs=dir([input_directory]);

-thetime=zeros(numel(thedirs)-1,1);

-names={};

-

-if(exist('OCTAVE_VERSION')) %check to see if running Octave or Matlab

-

-%This is Octave Version

-

-tic();

-k=6; %pick a k-mer size

-trainingmatrix=quikrTrain(trainingdatabasefilename,k); %this will return the training database

-disp('Training time:')

-[headers,~]=fastaread(trainingdatabasefilename); %read in the training database

-lambda=10000; 

-training_time=toc()

-

-species=struct();

-keys={};

-

-tic();

-

-

-i=0;

-%for numdirs=3:5

-for numdirs=3:numel(thedirs)

-i=i+1;

-disp([num2str(i) ' out of ' num2str(numel(thedirs)-2)])

-fastafilename=[input_directory '/' thedirs(numdirs).name];

-[loadfasta,~]=fastaread(fastafilename);

-numreads=numel(loadfasta);

-xstar=quikrCustomTrained(trainingmatrix,fastafilename,k,lambda);

-

-nonzeroentries=find(xstar); %get the indicies of the sequences quikr predicts are in your sample

-proportionscell=num2cell(xstar(nonzeroentries)); %convert the concentrations into a cell array

-namescell=headers(nonzeroentries); %Get the names of the sequences

-namesandproportions={namescell{:}; proportionscell{:}}; %This cell array contains the (unsorted) names of the reconstructed sequences and their concentrations (in the first and second columns respectively)

-

-[a cols]=size(namesandproportions);

-amount=zeros(cols,1);

-for j=1:cols

-  names{j}=['s' namesandproportions{1,j}];

-  amount(j)=namesandproportions{2,j};

-  if strcmp(keys,names{j})

-	temp=species.(names{j});

-      	temp(i)=round(amount(j).*numreads);

-      	species.(names{j})=temp;

-  else

-      temp=zeros(numel(thedirs)-3+1,1);

-      temp(i)=round(amount(j).*numreads);

-      species.(names{j})=temp;

-      keys{end+1}=names{j};

-  end

-end

-

-thefa=strfind(thedirs(numdirs).name,'.fa');

-

-if ~isempty(thedirs(numdirs).name(1:thefa-1))

-	sampleid{i}=thedirs(numdirs).name(1:thefa-1);

-else

-	sampleid{i}='empty_sampleid';

-end

-

-thetime(i+1)=toc();

-thetime(i+1)

-

-end

-

-disp('Total time to compute Quikr:')

-toc()

-disp('Quickr Average time per file:')

-mean(diff(thetime(1:i+1)))

-

-tic()

-numits=i;

-

-fid=fopen([output_directory '/' otu_table_name],'w');

-fprintf(fid,'# QIIME vGail OTU table\n');

-fprintf(fid,'#OTU_ID\t');

-for i=1:numits

-if i<numits

-fprintf(fid,'%s\t',sampleid{i});

-else

-fprintf(fid,'%s',sampleid{i});

-end

-end

-fprintf(fid,'\n');

-

-for k=1:numel(keys)

- fprintf(fid,'%s',keys{k}(2:end))

- temp(:,k)=species.(keys{k});

-        for i=1:numits

-                fprintf(fid,'\t%d',temp(i,k));

-        end

-fprintf(fid,'\n');

-end

-fclose(fid);

-

-disp('Time to output OTU Table:')

-toc()

-

-else

-

-%This is Matlab Version

-

-tic()

-k=6; %pick a k-mer size

-trainingmatrix=quikrTrain(trainingdatabasefilename,k); %this will return the training database

-'Training time:'

-[headers,~]=fastaread(trainingdatabasefilename); %read in the training database

-lambda=10000; 

-training_time=toc()

-

-species=containers.Map;

-

-tic()

-

-

-i=0;

-%for numdirs=3:5

-for numdirs=3:numel(thedirs)

-i=i+1;

-[num2str(i) ' out of ' num2str(numel(thedirs)-2)]

-fastafilename=[input_directory '/' thedirs(numdirs).name];

-[loadfasta,~]=fastaread(fastafilename);

-numreads=numel(loadfasta);

-xstar=quikrCustomTrained(trainingmatrix,fastafilename,k,lambda);

-

-nonzeroentries=find(xstar); %get the indicies of the sequences quikr predicts are in your sample

-proportionscell=num2cell(xstar(nonzeroentries)); %convert the concentrations into a cell array

-namescell=headers(nonzeroentries); %Get the names of the sequences

-namesandproportions={namescell{:}; proportionscell{:}}; %This cell array contains the (unsorted) names of the reconstructed sequences and their concentrations (in the first and second columns respectively)

-

-[a cols]=size(namesandproportions);

-amount=zeros(cols,1);

-for j=1:cols

-  names{j}=namesandproportions{1,j};

-  amount(j)=namesandproportions{2,j};

-  if isKey(species,names{j})

-	 temp=species(names{j});

-      temp(i)=round(amount(j).*numreads);

-      species(names{j})=temp;

-  else

-      temp=zeros(numel(thedirs)-3+1,1);

-      temp(i)=round(amount(j).*numreads);

-      species(names{j})=temp;

-  end

-end

-

-thefa=strfind(thedirs(numdirs).name,'.fa');

-

-if ~isempty(thedirs(numdirs).name(1:thefa-1))

-	sampleid{i}=thedirs(numdirs).name(1:thefa-1);

-else

-	sampleid{i}='empty_sampleid';

-end

-

-thetime(i+1)=toc();

-thetime(i+1)

-

-end

-

-'Total time to compute Quikr:'

-toc()

-'Quickr Average time per file:'

-mean(diff(thetime(1:i+1)))

-

-tic

-numits=i;

-

-fid=fopen([output_directory '/' otu_table_name],'w');

-fprintf(fid,'# QIIME vGail OTU table\n');

-fprintf(fid,'#OTU_ID\t');

-for i=1:numits

-if i<numits

-fprintf(fid,'%s\t',sampleid{i});

-else

-fprintf(fid,'%s',sampleid{i});

-end

-end

-fprintf(fid,'\n');

-

-thekeys=species.keys;

-for k=1:species.Count

- fprintf(fid,'%s',thekeys{k})

- temp(:,k)=species(thekeys{k});

-        for i=1:numits

-                fprintf(fid,'\t%d',temp(i,k));

-        end

-fprintf(fid,'\n');

-end

-fclose(fid);

-

-'Time to output OTU Table:'

-toc

-

-end