updated documentation , added multi_gg_final.m

2 files changed, 41 insertions, 6 deletions

diff --git a/doc/cli.markdown b/doc/cli.markdown
index 87df41b..d4a1bb9 100644
--- a/doc/cli.markdown
+++ b/doc/cli.markdown
@@ -8,7 +8,7 @@ module.
 ## Quikr\_train ##
 
 The quikr\_train is a tool to train a database for use with the quikr tool.
-Before running the quikr utility, you need to generate the trained matrix or
+Before running the quikr utility, you need to generate the sensing matrix or
 download a pretrained matrix from our database\_download.html.
 
 ### Usage ###
diff --git a/doc/matlab.markdown b/doc/matlab.markdown
index ca701b9..1e990d9 100644
--- a/doc/matlab.markdown
+++ b/doc/matlab.markdown
@@ -1,5 +1,4 @@
 # Quikr's Matlab Implementation #
-
 The Quikr implementation works in Matlab and also works in Octave, but the
 Octave version will run much slower
 
@@ -9,16 +8,15 @@ make sure that you are in the quikr's matlab directory (src/matlab/):
 
     cd quikr/src/matlab
 
-
 ### Using Quikr with the default databse ###
 This is the full path name to your data file:
 
     fastafilename='/path/to/quikr-code/testfastafile.fa';
 
 This will give the predicted reconstruction frequencies using the default
-training database trainset7\_112011.fa from RDP version 2.4
-Xstar will be on the same basis as trainset7\_112011.fa, so to get the sequences
-that are predicted to be present in your sample:
+training database trainset7\_112011.fa from RDP version 2.4 Xstar will be on the
+same basis as trainset7\_112011.fa, so to get the sequences that are predicted
+to be present in your sample:
 
     xstar=quikr(fastafilename);
 
@@ -110,3 +108,40 @@ This cell array contains the (unsorted) names of the reconstructed sequences and
 their concentrations (in the first and second columns respectively)
 
     namesandproportions={namescell{:}; proportionscell{:}};
+    
+### Using Multifasta2otu.m ###
+
+Usage tips:
+* Please name fasta files of sample reads with <sample id>.fa<*> and place them
+  into one directory without any other f ile in that directory (for example, no
+  hidden files that the operating system may generate, are allowed in that
+  direct ory)
+* 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 <train ing_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.  dp7\_mafft.fasttree,
+   gg\_94\_otus\_4feb2011.tre, etc.)
+3. your-defined <qiime_metadata_file.txt>
+
+The QIIME procedue:
+    convert_biom.py -i <quikr_otu_table.txt> -o <quikr_otu>.biom --biom_table_type="otu table"
+    beta_diversity.py -i <quikr_otu>.biom -m weighted_unifrac -o beta_div -t <tree file> (example: rdp7_mafft.fasttree)>
+    principal_coordinates.py -i beta_div/weighted_unifrac_<quikr_otu>.txt -o <quikr_otu_project_name>_weighted.txt
+    make_3d_plots.py -i <quikr_otu_project_name>_weighted.txt -o <3d_pcoa_plotdirectory> -m <qiime_metadata_file>