Matlab: move multifasta2otu into the main quikr folder

1 files changed, 219 insertions, 0 deletions

diff --git a/src/matlab/multifasta2otutable_gg94_2011.m b/src/matlab/multifasta2otutable_gg94_2011.m
new file mode 100644
index 0000000..9078487
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+++ b/src/matlab/multifasta2otutable_gg94_2011.m
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+% Quikr multifasta->otu_table_(for_qiime_use) wrapper code written by Gail Rosen -- 2/1/2013

+%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='../../../data/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);

+      if temp(i)==0

+         %insignificant counts, do nothing

+      else

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

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

+      end

+  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('Quikr 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);

+      if temp(i)==0

+	%insigficant counts, do nothing

+      else

+	species(names{j})=temp;

+      end

+  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()

+'Quikr 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