From 847cb4aa7f683254118605fd7f47d0251519e1ea Mon Sep 17 00:00:00 2001
From: Calvin <calvin@EESI>
Date: Mon, 11 Mar 2013 13:09:57 -0400
Subject: removed documented matlab, add bare makefile

---
 multifasta_documented.m | 129 ------------------------------------------------
 1 file changed, 129 deletions(-)
 delete mode 100644 multifasta_documented.m

(limited to 'multifasta_documented.m')

diff --git a/multifasta_documented.m b/multifasta_documented.m
deleted file mode 100644
index 5d82211..0000000
--- a/multifasta_documented.m
+++ /dev/null
@@ -1,129 +0,0 @@
-% 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='py-quikr/input/'; %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
-k=6; %pick a k-mer size
-addpath('quikr-code');
-
-% make output directory
-mkdir([output_directory]);
-
-% get a list of the input directory
-
-thedirs=dir([input_directory]);
-thetime=zeros(numel(thedirs)-1,1);
-names={};
-
-tic();
-
-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');
-
-% insert our header
-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');
-%
-
-
-% print our key and our values for each keys
-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()
-
-- 
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