Matlab: move multifasta2otu into the main quikr folder

3 files changed, 0 insertions, 458 deletions

diff --git a/src/matlab/multifasta2otu/README b/src/matlab/multifasta2otu/README
deleted file mode 100644
index 05a26c6..0000000
--- a/src/matlab/multifasta2otu/README
+++ /dev/null
@@ -1,27 +0,0 @@
-* Quikr multifasta->otu_table_(for_qiime_use) wrapper code written by Gail Rosen -- 2/1/2013
-
-Usage tips:
-* Please name fasta files of sample reads with <sample id>.fa<*> and place them into one directory without any other file in that directory (for example, no hidden files that the operating system may generate, are allowed in that directory)
-* Note: When making your QIIME Metadata file, the sample id's must match the fasta file prefix names
-* Fasta files of reads must have a suffix that starts with .fa (e.g.: .fasta and .fa are valid while .fna is NOT)
-* Modify the top of the Matlab/Octave scripts for <input_directory>, <output_directory>, <output_filename>, and <training_database_filename>
-
-To use with QIIME, one must run the QIIME conversion tool on our OTU table output:
-convert_biom.py -i <quikr_otu_table.txt> -o <quikr_otu>.biom --biom_table_type="otu table"
-
----------------------------
-
-4-step QIIME procedure after using Quikr to obtain 3D PCoA graphs:
-(Note: Our code works much better with WEIGHTED Unifrac as opposed to 
-Unweighted.)
-
-Pre-requisites:
-1. <quikr_otu_table.txt>
-2. the tree of the database sequences that were used (e.g. rdp7_mafft.fasttree, gg_94_otus_4feb2011.tre. they are in the data directory)
-3. your-defined <qiime_metadata_file.txt>
-
-1. convert_biom.py -i <quikr_otu_table.txt> -o <quikr_otu>.biom --biom_table_type="otu table"
-2. beta_diversity.py -i <quikr_otu>.biom -m weighted_unifrac -o beta_div -t <tree file (example: rdp7_mafft.fasttree)>
-3. principal_coordinates.py -i beta_div/weighted_unifrac_<quikr_otu>.txt -o <quikr_otu_project_name>_weighted.txt
-4. make_3d_plots.py -i <quikr_otu_project_name>_weighted.txt -o <3d_pcoa_plotdirectory> -m <qiime_metadata_file>
-
diff --git a/src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m b/src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m
deleted file mode 100644
index 9078487..0000000
--- a/src/matlab/multifasta2otu/multifasta2otutable_gg94_2011.m
+++ /dev/null
@@ -1,219 +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='../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

diff --git a/src/matlab/multifasta2otu/multifasta2otutable_rdp7.m b/src/matlab/multifasta2otu/multifasta2otutable_rdp7.m
deleted file mode 100644
index 9c83ea9..0000000
--- a/src/matlab/multifasta2otu/multifasta2otutable_rdp7.m
+++ /dev/null
@@ -1,212 +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 Quikr on the default RDP_7 training set

-

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

-%Do not have to define trainingdatabase file here

-[headers,~]=fastaread('../../../data/trainset7_112011.fa'); %read in the 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

-

-

-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=quikr(fastafilename); %this will give the predicted reconstruction frequencies

-

-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}(1:strfind(namesandproportions{1,j},' ')-1);

-  names{j}=strrep(names{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

-	% do not make a key -- has insignificant counts

-      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)

-truncname=strrep(keys{k},'_','|');

-  fprintf(fid,'%s',truncname);

-

-  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 the Matlab version

-

-

-species=containers.Map;

-

-tic()

-

-i=0;

-%for numdirs=3:6

-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=quikr(fastafilename); %this will give the predicted reconstruction frequencies

-

-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

-	% insignificant counts -- do not put

-      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

-if k==1

-  space=thekeys{k}(20);

-end

-delimit =strfind(thekeys{k},space);

-truncname=thekeys{k}(1:delimit-1);

-  fprintf(fid,'%s',truncname);

-

-  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