Exploring SanXoT features - Revision history

Mtrevisan at 10:36, 17 August 2018

2018-08-17T10:36:05Z

Mtrevisan at 10:29, 17 August 2018

2018-08-17T10:29:12Z

Mtrevisan at 10:12, 17 August 2018

2018-08-17T10:12:51Z

Mtrevisan: /* Last step for experiment integration */

2018-08-17T10:11:32Z

‎Last step for experiment integration

Mtrevisan: /* Modifications needed */

2018-08-17T10:10:56Z

‎Modifications needed

Mtrevisan at 10:10, 17 August 2018

2018-08-17T10:10:25Z

Mtrevisan at 10:05, 17 August 2018

2018-08-17T10:05:09Z

Mtrevisan: /* Getting the quantification of proteins */

2018-08-17T10:03:30Z

‎Getting the quantification of proteins

Mtrevisan at 10:03, 17 August 2018

2018-08-17T10:03:02Z

Show changes

Mtrevisan at 15:23, 14 August 2018

2018-08-14T15:23:31Z

@@ Line 478: / Line 478: @@
 <syntaxhighlight lang="dos" line start=39>
-sanxotgauss.exe -agauss -p%workingFolder% -rdbcats_rels.xls -clist_outList.xls -zqaMerged_outStats.xls -g
+sanxotgauss.exe -agauss -p%workingFolder% -rdbcats_rels.tsv -clist_outList.tsv -zqaMerged_outStats.tsv -g
 </syntaxhighlight>
 The options are the same as in the previous Sanson command, excluding -g (which here is used to ask the program not to show the graph, just save it, which is important to keep the workflow going).
 === Calculating the degree of coordination using the Coordinometer ===
@@ Line 487: / Line 494: @@
 We have defined a parameter called '''''degree of coordination''''', which tells us objectively how much a proteome is coordinated. This parameter takes into account the number of outlier proteins (proteins that are expressed differently, compared to proteins classified within the same category), and how much are categories changing. The '''[[Coordinometer]]''' calculates this number using this command:
+<syntaxhighlight lang="dos" line start=41>
-<syntaxhighlight lang="dos" line start=40>
+coordinometer.exe -acoordination -p%workingFolder% -qqc_noOuts_outStats.tsv -cca_outStats.tsv
 </syntaxhighlight>
-The Coordinometer extracts the number of relations tagged as outliers (using the "out" tag in the third column) in file qc_noOuts_outStats.xls (using option -q, for proteins expressed differently compared to other proteins in the same categories, a calculation from the "top-left" side of the triangle) and gets the number of categories changing using file ca_outStats.xls (using option -c, and reading directly the FDR column). The latter is set by default to 5%, but a different threshold can be set using the --cafdr option.
+The Coordinometer extracts the number of relations tagged as outliers (using the "out" tag in the third column) in file qc_noOuts_outStats.tsv (using option -q, for proteins expressed differently compared to other proteins in the same categories, a calculation from the "top-left" side of the triangle) and gets the number of categories changing using file ca_outStats.tsv (using option -c, and reading directly the FDR column). The latter is set by default to 5%, but a different threshold can be set using the --cafdr option.
 The result is given both printed in the console, and in the corresponding log file, coordination_logFile.txt, along with the information used to calculate it:
-  Total number of changing categories: 66
+  Total number of changing categories: 35
-  Total number of relations pointing to changing categories: 106
+  Total number of relations pointing to changing categories: 93
-  Total number of outlier relations pointing to changing categories: 2
+  Total number of outlier relations pointing to changing categories: 1
-  Total number of outlier relations pointing to non-changing categories: 46
+  Total number of outlier relations pointing to non-changing categories: 18
-  Degree of coordination: 0.684211
+  Degree of coordination: 0.828829
-Where 0.684211 is the result of:
+Where 0.828829 is the result of:
   (numRelsChangingCats - numOutliersChangingCats) / (numRelsChangingCats + numOutliersNonChangingCats) =
-  (106 - 2) / (106 + 46) = 0.684211
+  (93 - 1) / (93 + 18) = 0.828829
 ==References==

@@ Line 335: / Line 335: @@
 == Test 3: Systems biology ==
-In this final test we make a systems biology analysis of the previous merged experiment using the Systems Biology Triangle method. For that we start with the files generated by the Test 2, plus a tab-separated table directly downloaded from the '''[https://en.wikipedia.org/wiki/DAVID DAVID bioinformatics resource]'''.
+In this final test we make a systems biology analysis of the previous merged experiment using the Systems Biology Triangle method. For that we start with the files generated by the Test 2, plus a tab-separated table directly downloaded from the '''[https://en.wikipedia.org/wiki/DAVID DAVID bioinformatics resource]''' (the file ''SB_Homo_19dic-2017.txt'').
 <div class="toccolours mw-collapsible mw-collapsed">
@@ Line 402: / Line 402: @@
 Here we use, as in all SanXoT modules, option -a and -p as usual (to define a prefix for the output files in this operation and setting the working directory, respectively). Then we give the filename of the tab-separated file downloaded from DAVID (using -d), we indicate the column number (one-based) containing the protein accession number (option -q, in this case -q1 indicates it is the first column), and the column containing the category we want to parse to the relations file (using -c, in this case we want to parse the data from BioCarta, which are in column 5), and we define a prefix for the category (as these are categories from BioCarta, we use -f to set this to "BioCarta_", where the underscore is used as separator to make it more human-readable). The prefix is not mandatory, but highly recommended when we import data from different databases, as some category names may have the same name but different protein sets; using a prefix not only avoids overlapping, but also helps track back from which database are the categories in use.
-The file fastaHeadersNoSequences.fasta is not mandatory. In this case, as we want to use as protein identifier the full FASTA header (for using more human-readable results), we include it. Obviously, this must contain the same identifiers as the ones used for DAVID. Typically, the FASTA file used to identify the proteins should be used, but in this case we have removed the sequences to avoid giving the users a too large file in this test.
+As a result, we get the file dbcats1_rels.tsv. Now we concatenate the relations related to the databases for [http://www.geneontology.org Gene Ontology] (for BP = biological process, CC = cellular component, and MF = molecular function), [http://www.genome.jp/kegg KEGG] and [https://pir.georgetown.edu PIR]. For each of them we use Camacho, changing only the -c option (to set the column), -f (for the different category prefix) and including the file to which they concatenate (using the option -x). Other users might just prefer to overwrite the original file, but, in order to keep track of the different operations, we just use a different file for each output.
 <syntaxhighlight lang="dos" line start=18>
-camacho.exe -adbcats2 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats1_rels.xls" -c6 -fGOBP_
+camacho.exe -adbcats2 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats1_rels.tsv" -c6 -fGOBP_
-camacho.exe -adbcats3 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats2_rels.xls" -c7 -fGOCC_
+camacho.exe -adbcats3 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats2_rels.tsv" -c7 -fGOCC_
-camacho.exe -adbcats4 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats3_rels.xls" -c8 -fGOMF_
+camacho.exe -adbcats4 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats3_rels.tsv" -c8 -fGOMF_
-camacho.exe -adbcats5 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats4_rels.xls" -c9 -fKEGG_
+camacho.exe -adbcats5 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats4_rels.tsv" -c9 -fKEGG_
-camacho.exe -adbcats -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats5_rels.xls" -c10 -fPIR_
+camacho.exe -adbcats -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats5_rels.tsv" -c10 -fPIR_
 </syntaxhighlight>
-We end up with the final file dbcats_rels.xls; this relations file can now be used for as many Systems Biology Triangle analyses as needed, without needing to use Camacho again (unless we want to add databases or an updated ontology from DAVID).
+We end up with the final file dbcats_rels.tsv; this relations file can now be used for as many Systems Biology Triangle analyses as needed, without needing to use Camacho again (unless we want to add databases or an updated ontology from DAVID).
 === From proteins to categories ===
-We start using the relations file just generated, dbcats_rels.xls, to integrate proteins into categories. Every protein has a different weight, depending on the quality of the scans, the number of peptides detected and quantified for the proteins, and perhaps even the different experiments where they have been found. As a result, the information from each protein has a different weight in the category where they have been classified (in the same way as happened in the previous levels). Getting the category-level information (rather than a lengthy list of quantified proteins) will help us getting the big picture of what is going on in the proteome. Additionally, using the Systems Biology Triangle, we integrate the information of all the proteins quantified, not just a list of proteins whose quantification exceeds a defined threshold (as in Over-Representation Algorithms).
+We start using the relations file just generated, dbcats_rels.tsv, to integrate proteins into categories. Every protein has a different weight, depending on the quality of the scans, the number of peptides detected and quantified for the proteins, and perhaps even the different experiments where they have been found. As a result, the information from each protein has a different weight in the category where they have been classified (in the same way as happened in the previous levels). Getting the category-level information (rather than a lengthy list of quantified proteins) will help us getting the big picture of what is going on in the proteome. Additionally, using the Systems Biology Triangle, we integrate the information of all the proteins quantified, not just a list of proteins whose quantification exceeds a defined threshold (as in Over-Representation Algorithms).
 We integrate in the usual manner, first getting the variance of the proteins, then tagging outliers (proteins that behave differently than other proteins classified in the same group) and finally performing the integration with the formerly calculated variance and excluding the outlier proteins:
 <syntaxhighlight lang="dos" line start=28>
-sanxot.exe -aqc -p%workingFolder% -dmerged_noOuts_higherLevel.xls -rdbcats_rels.xls -g
+sanxot.exe -aqc -p%workingFolder% -dmerged_noOuts_higherLevel.tsv -rdbcats_rels.tsv -g
-sanxotsieve.exe -aoutsqc -p%workingFolder% -dmerged_noOuts_higherLevel.xls -rdbcats_rels.xls -Vqc_infoFile.txt -f0.01
+sanxotsieve.exe -aoutsqc -p%workingFolder% -dmerged_noOuts_higherLevel.tsv -rdbcats_rels.tsv -Vqc_infoFile.txt -f0.01
-sanxot.exe -aqc_noOuts -p%workingFolder% -dmerged_noOuts_higherLevel.xls -routsqc_tagged.xls -fg -Vqc_infoFile.txt --tags="!out"
+sanxot.exe -aqc_noOuts -p%workingFolder% -dmerged_noOuts_higherLevel.tsv -routsqc_tagged.tsv -fg -Vqc_infoFile.txt --tags="!out"
 </syntaxhighlight>
 This operation is the "top-left" side of the Systems Biology Triangle.
-The last integration, where categories are compared to each other, is the "top-right" side of the triangle:
+The last integration, where categories are compared to each other, is the "top-right" side of the triangle (see '''[[How to use SanXoT]]''' for more information):
 <syntaxhighlight lang="dos" line start=34>
-sanxot.exe -aca -p%workingFolder% -dqc_noOuts_higherLevel.xls -Cg
+sanxot.exe -aca -p%workingFolder% -dqc_noOuts_higherLevel.tsv -Cg
 </syntaxhighlight>
@@ Line 440: / Line 438: @@
 === Filtering the most relevant categories using SanXoTSqueezer ===
-Now we have the information needed to get some details about the analysis. Sometimes we have too many categories, so the analysis becomes difficult. We could make "supercategories" using a relations file between category and supercategory, but in this case we will just filter out the categories with '''[[SanXoTSqueezer]]''', getting those with a minimum number of 3 proteins (using the option -n) quantified and a maximum FDR of 5% (using the option -f). We also need the two outStats files for the protein-to-category ("qc", using option -l), and the category-to-all ("ca", using the optioni -u) operations, using files from previous steps (qc_noOuts_outStats.xls and ca_outStats.xls, respectively):
+Now we have the information needed to get some details about the analysis. Sometimes we have too many categories, so the analysis becomes difficult. We could make "supercategories" using a relations file between category and supercategory, but in this case we will just filter out the categories with '''[[SanXoTSqueezer]]''', getting those with a minimum number of 3 proteins (using the option -n) quantified and a maximum FDR of 5% (using the option -f). We also need the two outStats files for the protein-to-category ("qc", using option -l), and the category-to-all ("ca", using the optioni -u) operations, using files from previous steps (qc_noOuts_outStats.tsv and ca_outStats.tsv, respectively):
 <syntaxhighlight lang="dos" line start=37>
-sanxotsqueezer.exe -alist -p%workingFolder% -lqc_noOuts_outStats.xls -uca_outStats.xls -f0.05 -n3
+sanxotsqueezer.exe -alist -p%workingFolder% -lqc_noOuts_outStats.tsv -uca_outStats.tsv -f0.05 -n3
 </syntaxhighlight>
-The output is a list of categories, with some information associated, in file list_outList.xls; we will analyse the categories in this list.
+The output is a list of categories, with some information associated, in file list_outList.tsv; we will analyse the categories in this list.
 === Clustering together similar categories using Sanson ===
@@ Line 464: / Line 462: @@
 <syntaxhighlight lang="dos" line start=38>
-sanson.exe -asanson -p%workingFolder% -rdbcats_rels.xls -clist_outList.xls -zqaMerged_outStats.xls -gpdf
+sanson.exe -asanson -p%workingFolder% -rdbcats_rels.tsv -clist_outList.tsv -zqaMerged_outStats.tsv -gpdf
 </syntaxhighlight>
-Where we already know the relations file dbcats_rels.xls (option -r); the list_outList.xls (option -c) generated in the previous step is important to provide the categories that will be considered; and the outStats file qaMerged_outStats.xls (option -z) is the file providing the protein-level, category-independent quantifications (generated in the last command of Test 2), which is important to provide the protein information for each category.
+Where we already know the relations file dbcats_rels.tsv (option -r); the list_outList.tsv (option -c) generated in the previous step is important to provide the categories that will be considered; and the outStats file qaMerged_outStats.tsv (option -z) is the file providing the protein-level, category-independent quantifications (generated in the last command of Test 2), which is important to provide the protein information for each category.
-The output of Sanson is a .gv file (here sanson_simGraph.gv), where category clusters are given in [[http://en.wikipedia.org/wiki/DOT_language DOT language]]; they can be interpreted by open-source programs like [http://graphviz.org/ Graphviz] to generate an image file. If Graphviz is installed, Sanson can launch itself the interpretation (you only need to specify in the dot.ini file the location where the dot.exe file used by Graphviz is). In this test, we have used as output the pdf file sanson_simGraph.pdf (using the -g option), but it can be also interesting generating an [https://en.wikipedia.org/wiki/Scalable_Vector_Graphics svg] file, which (when opened by suitable browsers) can give back the list of protein identifiers present in each category just by hovering the mouse over the category box.
+The output of Sanson is a .gv file (here sanson_simGraph.gv), where category clusters are given in [[http://en.wikipedia.org/wiki/DOT_language DOT language]]; they can be interpreted by open-source programs like [http://graphviz.org/ Graphviz] to generate an image file. If Graphviz is installed, Sanson can launch itself the interpretation (if you are using Windows, you only need to specify in the dot.ini file the location where the dot.exe file used by Graphviz is). In this test, we have used as output the pdf file sanson_simGraph.pdf (using the -g option), but it can be also interesting generating an [https://en.wikipedia.org/wiki/Scalable_Vector_Graphics svg] file, which (when opened by suitable browsers) can give back the list of protein identifiers present in each category just by hovering the mouse over the category box.
 As you will see, each category box has a number of customisable colour columns. Each column represent a protein present in the category, and its colour depends on the standarised logfold-change (Z, the standardised version of X=logfold, which means that X has been centred and corrected according to the variance) of its protein. By default, white colour means no change, green means there is more quantity from sample B than from sample A (as logarithm(A/B) < 0), and, conversely, red means there is more quantity from sampe A that from sample B. The arrows have numbers that indicate the total numbers of proteins in a category that are present in the category the arrow is pointing to. Absence of arrow does not mean they do not share proteins, it just means that the ratio of proteins shared does not reach the calculated threshold specified above.

@@ Line 357: / Line 357: @@
 REM
-camacho.exe -adbcats1 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" --fasta="fastaHeadersNoSequences.fasta" -q1 -c5 -fBioCarta_
+camacho.exe -adbcats1 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 -c5 -fBioCarta_
-camacho.exe -adbcats2 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats1_rels.xls" -c6 -fGOBP_
+camacho.exe -adbcats2 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 -x"dbcats1_rels.tsv" -c6 -fGOBP_
-camacho.exe -adbcats3 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats2_rels.xls" -c7 -fGOCC_
+camacho.exe -adbcats3 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 -x"dbcats2_rels.tsv" -c7 -fGOCC_
-camacho.exe -adbcats4 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats3_rels.xls" -c8 -fGOMF_
+camacho.exe -adbcats4 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 -x"dbcats3_rels.tsv" -c8 -fGOMF_
-camacho.exe -adbcats5 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats4_rels.xls" -c9 -fKEGG_
+camacho.exe -adbcats5 -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 -x"dbcats4_rels.tsv" -c9 -fKEGG_
-camacho.exe -adbcats -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 --fasta="fastaHeadersNoSequences.fasta" -x"dbcats5_rels.xls" -c10 -fPIR_
+camacho.exe -adbcats -p%workingFolder% -d"SB_Homo_19dic-2017.txt" -q1 -x"dbcats5_rels.tsv" -c10 -fPIR_
 REM
@@ Line 368: / Line 368: @@
 REM
-sanxot.exe -aqc -p%workingFolder% -dmerged_noOuts_higherLevel.xls -rdbcats_rels.xls -g
+sanxot.exe -aqc -p%workingFolder% -dmerged_noOuts_higherLevel.tsv -rdbcats_rels.tsv -g
-sanxotsieve.exe -aoutsqc -p%workingFolder% -dmerged_noOuts_higherLevel.xls -rdbcats_rels.xls -Vqc_infoFile.txt -f0.01
+sanxotsieve.exe -aoutsqc -p%workingFolder% -dmerged_noOuts_higherLevel.tsv -rdbcats_rels.tsv -Vqc_infoFile.txt -f0.01
-sanxot.exe -aqc_noOuts -p%workingFolder% -dmerged_noOuts_higherLevel.xls -routsqc_tagged.xls -fg -Vqc_infoFile.txt --tags="!out"
+sanxot.exe -aqc_noOuts -p%workingFolder% -dmerged_noOuts_higherLevel.tsv -routsqc_tagged.tsv -fg -Vqc_infoFile.txt --tags="!out"
 REM cat to all
-sanxot.exe -aca -p%workingFolder% -dqc_noOuts_higherLevel.xls -Cg
+sanxot.exe -aca -p%workingFolder% -dqc_noOuts_higherLevel.tsv -Cgf -v0
-sanxotsqueezer.exe -alist -p%workingFolder% -lqc_noOuts_outStats.xls -uca_outStats.xls -f0.05 -n3
+sanxotsqueezer.exe -alist -p%workingFolder% -lqc_noOuts_outStats.tsv -uca_outStats.tsv -f0.05 -n3
-sanson.exe -asanson -p%workingFolder% -rdbcats_rels.xls -clist_outList.xls -zqaMerged_outStats.xls -gpdf
+sanson.exe -asanson -p%workingFolder% -rdbcats_rels.tsv -clist_outList.tsv -zqaMerged_outStats.tsv -gpdf
-sanxotgauss.exe -agauss -p%workingFolder% -rdbcats_rels.xls -clist_outList.xls -zqaMerged_outStats.xls -g
+sanxotgauss.exe -agauss -p%workingFolder% -rdbcats_rels.tsv -clist_outList.tsv -zqaMerged_outStats.tsv -g
-coordinometer.exe -acoordination -p%workingFolder% -qqc_noOuts_outStats.xls -cca_outStats.xls
+sanxotgauss.exe -agauss_noLegend -p%workingFolder% -rdbcats_rels.tsv -clist_outList.tsv -zqaMerged_outStats.tsv -gk
 </syntaxhighlight>
 </div>

@@ Line 320: / Line 320: @@
 sanxot.exe -amerged -p%workingFolder% -dmerge_newData.txt -rmerge_newRels.txt -g
 sanxotsieve.exe -aoutsMerged -p%workingFolder% -dmerge_newData.txt -rmerge_newRels.txt -Vmerged_infoFile.txt -f0.01
-sanxot.exe -amerged_noOuts -p%workingFolder% -dmerge_newData.txt -routsMerged_tagged.xls -fg -Vmerged_infoFile.txt --tags="!out"
+sanxot.exe -amerged_noOuts -p%workingFolder% -dmerge_newData.txt -routsMerged_tagged.tsv -fg -Vmerged_infoFile.txt --tags="!out"
-sanxot.exe -aqaMerged -p%workingFolder% -dmerged_noOuts_higherLevel.xls -Cg
+sanxot.exe -aqaMerged -p%workingFolder% -dmerged_noOuts_higherLevel.tsv -Cg
 </syntaxhighlight>
@@ Line 329: / Line 329: @@
 :* then SanXoTSieve tags the outliers (generating the outsMerged_tagged.tsv file with the same relations as merge_newRels.txt, but incliding the tag "out" in the third column),
 :* the third line integrates the proteins using the relations file and the variance from the two previous commands;
-:* finally, all the experiment-independent proteins are compared to each other, getting the variance (stored in merged_noOuts_infoFile.txt) and providing an FDR that can be used to filter changing proteins (in file merged_noOuts_outStats.xls).
+:* finally, all the experiment-independent proteins are compared to each other, getting the variance (stored in merged_noOuts_infoFile.txt) and providing an FDR that can be used to filter changing proteins (in file merged_noOuts_outStats.tsv).
 The last step will also be used in the Systems Biology Triangle method (SBT)<ref>García-Marqués, F., Trevisan-Herraz, M., Martínez-Martínez, S., Camafeita, E., Jorge, I., Lopez, J.A., Méndez-Barbero, N., Méndez-Ferrer, S., del Pozo, M.A., Ibáñez, B., Andrés, V., Sánchez-Madrid, F., Redondo, J.M., Bonzon-Kulichenko, E. and Vázquez, J. (2016) '''[http://www.mcponline.org/content/15/5/1740.long A novel systems-biology algorithm for the analysis of coordinated protein responses using quantitative proteomics]'''. ''Molecular & Cellular Proteomics''.</ref> which we will see now (the "protein-to-all" operation is the "bottom side" of the triangle).

@@ Line 70: / Line 70: @@
 </syntaxhighlight>
-In the first operations, we will parse the data from the ''startingFile.xls'' using '''[[Aljamia]]''' to parse the relations files. In this case, the input file is a tab-separated text file quantified using [http://www.thermofisher.com/order/catalog/product/OPTON-30795 Proteome Discoverer 2.1], but any other tab-separated text file which arranges information in columns with headers can be used as input (for example [https://en.wikipedia.org/wiki/SQLite SQLite] query results from Proteome Discoverer's MSF files, or results from [https://en.wikipedia.org/wiki/Mascot_(software) Mascot]).
+In the first operations, we will parse the data from the starting file (170415_Marga_GBS_iTRAQ_PSMs.txt) using '''[[Aljamia]]''' to parse the relations files. In this case, the input file is a tab-separated text file quantified using [http://www.thermofisher.com/order/catalog/product/OPTON-30795 Proteome Discoverer 2.1], but any other tab-separated text file which arranges information in columns with headers can be used as input (for example [https://en.wikipedia.org/wiki/SQLite SQLite] query results from Proteome Discoverer's MSF files, or results from [https://en.wikipedia.org/wiki/Mascot_(software) Mascot]).
 === Parsing the data ===

@@ Line 218: / Line 218: @@
 === A parallel anaysis ===
-In this example we merge just two experiments for simplicity, but it is possible to merge even hundreds of them at once.
+In this example we merge just two experiments for simplicity, but it is possible to merge even hundreds of them at once (such as hundreds of technical or biological replicates).
 Command lines 6-8 have already been discussed, they must be adapted to the paths of the user in his/her computer. Lines 14-29 are actually doing the same as discussed in the previous section, but for the 115 vs 119 comparative:
@@ Line 226: / Line 226: @@
 REM
-klibrate.exe -astart115vs119 -p%workingFolder% -dsdata115vs119_table.xls -rsprels_table.xls -g
+klibrate.exe -astart115vs119 -p%workingFolder% -dsdata115vs119_table.tsv -rsprels_table.tsv -g
-sanxot.exe -asp115vs119 -p%workingFolder% -dstart115vs119_calibrated.xls -rsprels_table.xls -g
+sanxot.exe -asp115vs119 -p%workingFolder% -dstart115vs119_calibrated.tsv -rsprels_table.tsv -g
-sanxotsieve.exe -aspouts115vs119 -p%workingFolder% -dstart115vs119_calibrated.xls -rsprels_table.xls -Vsp115vs119_infoFile.txt -f0.01
+sanxotsieve.exe -aspouts115vs119 -p%workingFolder% -dstart115vs119_calibrated.tsv -rsprels_table.tsv -Vsp115vs119_infoFile.txt -f0.01
-sanxot.exe -asp115vs119_noOuts -p%workingFolder% -dstart115vs119_calibrated.xls -rspouts115vs119_tagged.xls -Vsp115vs119_infoFile.txt -fg --tags="!out"
+sanxot.exe -asp115vs119_noOuts -p%workingFolder% -dstart115vs119_calibrated.tsv -rspouts115vs119_tagged.tsv -Vsp115vs119_infoFile.txt -fg --tags="!out"
 REM peptide to protein for 115vs119
-sanxot.exe -apq115vs119 -p%workingFolder% -dsp115vs119_noOuts_higherLevel.xls -rpqrels_table.xls -g
+sanxot.exe -apq115vs119 -p%workingFolder% -dsp115vs119_noOuts_higherLevel.tsv -rpqrels_table.tsv -g
-sanxotsieve.exe -apqouts115vs119 -p%workingFolder% -dsp115vs119_noOuts_higherLevel.xls -rpqrels_table.xls -Vpq115vs119_infoFile.txt -f0.01
+sanxotsieve.exe -apqouts115vs119 -p%workingFolder% -dsp115vs119_noOuts_higherLevel.tsv -rpqrels_table.tsv -Vpq115vs119_infoFile.txt -f0.01
-sanxot.exe -apq115vs119_noOuts -p%workingFolder% -dsp115vs119_noOuts_higherLevel.xls -rpqouts115vs119_tagged.xls -fg -Vpq115vs119_infoFile.txt --tags="!out"
+sanxot.exe -apq115vs119_noOuts -p%workingFolder% -dsp115vs119_noOuts_higherLevel.tsv -rpqouts115vs119_tagged.tsv -fg -Vpq115vs119_infoFile.txt --tags="!out"
 REM see protein changes for 115vs119
-sanxot.exe -aqa115vs119 -p%workingFolder% -dpq115vs119_noOuts_higherLevel.xls -Cg
+sanxot.exe -aqa115vs119 -p%workingFolder% -dpq115vs119_noOuts_higherLevel.tsv -Cg
 </syntaxhighlight>
@@ Line 252: / Line 252: @@
   tag		file
-vs118	qa114vs118_lowerNormV.xls
+vs118	qa114vs118_lowerNormV.tsv
-vs119	qa115vs119_lowerNormV.xls
+vs119	qa115vs119_lowerNormV.tsv
 The column "tag" includes the suffix that will be appended to each protein (or other) identifier, while the column "file" contains the filenames of the data files; these files are similar, but distinct, from _higherLevel files: they are their centred versions (for example, the one with prefix qa114vs118 has been generated in the last step of the 114vs118 integration, and the suffix _lowerNormV tells us it is a data file where the fold-changes have been centred subtracting the grand mean, X'inf, and the weights have been kept unchanged; files with suffix _lowerNorm'''W''' have identical first and second column, but the third column, for weights has been modified to include the general variance of the protein level, as described in the WSPP statistical model).
@@ Line 327: / Line 327: @@
 The approach here is the same as for the integration from spectrum to peptide or peptide to protein (but this case we integrate from "experiment-dependent protein data" to "experiment-independent protein data"):
 :* the first line calculates the variance (storing it in the merged_infoFile.txt file)
-:* then SanXoTSieve tags the outliers (generating the outsMerged_tagged.xls file with the same relations as merge_newRels.txt, but incliding the tag "out" in the third column),
+:* then SanXoTSieve tags the outliers (generating the outsMerged_tagged.tsv file with the same relations as merge_newRels.txt, but incliding the tag "out" in the third column),
 :* the third line integrates the proteins using the relations file and the variance from the two previous commands;
 :* finally, all the experiment-independent proteins are compared to each other, getting the variance (stored in merged_noOuts_infoFile.txt) and providing an FDR that can be used to filter changing proteins (in file merged_noOuts_outStats.xls).

@@ Line 18: / Line 18: @@
 REM commands
-set unit=D:
+ set unit=D:
-set programFolder="D:\SSP"
+ set programFolder="D:\SSP"
-set workingFolder="D:\SanXoT_test1"
+ set workingFolder="D:\SanXoT_test1"
 %unit%
@@ Line 26: / Line 27: @@
 REM create the relation files
-aljamia.exe -asprels -p%workingFolder% -xstartingFile.xls -R24 -i"[Sequence]" -j"[RAWFileName]-[FirstScan]-[Charge]"
+aljamia.exe -asprels -p%workingFolder% -x%startingFile% -i"[Annotated Sequence]_[Modifications]" -j"[Spectrum File]-[First Scan]-[Charge]" -f"[Confidence]==High || [Confidence]==Medium"
-aljamia.exe -apqrels -p%workingFolder% -xstartingFile.xls -R24 -i"[FASTAProteinDescription]" -j"[Sequence]"
+aljamia.exe -apqrels -p%workingFolder% -x%startingFile% -i"'[Master Protein Accessions]'.split(';')[0]" -j"[Annotated Sequence]_[Modifications]" -f"[Confidence]==High || [Confidence]==Medium" -A"i"
 REM create the data file at the scan level
-aljamia.exe -asdata114vs118 -p%workingFolder% -xstartingFile.xls -R24 -i"[RAWFileName]-[FirstScan]-[Charge]" -j"math.log([q_reporterIon_114]/[q_reporterIon_118], 2)" -k"max([q_reporterIon_114], [q_reporterIon_118])" -A"jk"
+aljamia.exe -asdata114vs118 -p%workingFolder% -x%startingFile% -i"[Spectrum File]-[First Scan]-[Charge]" -j"math.log([114]/[118], 2)" -k"max([114], [118])" -f"[Confidence]==High || [Confidence]==Medium" -A"jk"
-aljamia.exe -asdata115vs119 -p%workingFolder% -xstartingFile.xls -R24 -i"[RAWFileName]-[FirstScan]-[Charge]" -j"math.log([q_reporterIon_115]/[q_reporterIon_119], 2)" -k"max([q_reporterIon_115], [q_reporterIon_119])" -A"jk"
+aljamia.exe -asdata115vs119 -p%workingFolder% -x%startingFile% -i"[Spectrum File]-[First Scan]-[Charge]" -j"math.log([115]/[119], 2)" -k"max([115], [119])" -f"[Confidence]==High || [Confidence]==Medium" -A"jk"
 REM The following tab-separated text files (despite their extensions) are generated:
-REM sprels_table.xls, with two columns, having scan-to-peptide identifiers (rawfile-scanNumber-charge for scans, sequence for peptides)
+REM sprels_table.tsv, with two columns, having scan-to-peptide identifiers (rawfile-scanNumber-charge for scans, sequence for peptides)
-REM pqrels_table.xls, with two columns, having peptide-to-protein identifiers (text of their fasta header as protein identifier)
+REM pqrels_table.tsv, with two columns, having peptide-to-protein identifiers (text of their fasta header as protein identifier)
-REM sdata_table.xls, with three columns having {scan identifier, log2(foldchange), weight}
+REM sdata_table.tsv, with three columns having {scan identifier, log2(foldchange), weight}
-klibrate.exe -astart114vs118 -p%workingFolder% -dsdata114vs118_table.xls -rsprels_table.xls -g
+klibrate.exe -astart114vs118 -p%workingFolder% -dsdata114vs118_table.tsv -rsprels_table.tsv -g
-sanxot.exe -asp114vs118 -p%workingFolder% -dstart114vs118_calibrated.xls -rsprels_table.xls -g
+sanxot.exe -asp114vs118 -p%workingFolder% -dstart114vs118_calibrated.tsv -rsprels_table.tsv -g
-sanxotsieve.exe -aspouts114vs118 -p%workingFolder% -dstart114vs118_calibrated.xls -rsprels_table.xls -Vsp114vs118_infoFile.txt -f0.01
+sanxotsieve.exe -aspouts114vs118 -p%workingFolder% -dstart114vs118_calibrated.tsv -rsprels_table.tsv -Vsp114vs118_infoFile.txt -f0.01
-sanxot.exe -asp114vs118_noOuts -p%workingFolder% -dstart114vs118_calibrated.xls -rspouts114vs118_tagged.xls -Vsp114vs118_infoFile.txt -fg --tags="!out"
+sanxot.exe -asp114vs118_noOuts -p%workingFolder% -dstart114vs118_calibrated.tsv -rspouts114vs118_tagged.tsv -Vsp114vs118_infoFile.txt -fg --tags="!out"
 REM peptide to protein
-sanxot.exe -apq114vs118 -p%workingFolder% -dsp114vs118_noOuts_higherLevel.xls -rpqrels_table.xls -g
+sanxot.exe -apq114vs118 -p%workingFolder% -dsp114vs118_noOuts_higherLevel.tsv -rpqrels_table.tsv -g
-sanxotsieve.exe -apqouts114vs118 -p%workingFolder% -dsp114vs118_noOuts_higherLevel.xls -rpqrels_table.xls -Vpq114vs118_infoFile.txt -f0.01
+sanxotsieve.exe -apqouts114vs118 -p%workingFolder% -dsp114vs118_noOuts_higherLevel.tsv -rpqrels_table.tsv -Vpq114vs118_infoFile.txt -f0.01
-sanxot.exe -apq114vs118_noOuts -p%workingFolder% -dsp114vs118_noOuts_higherLevel.xls -rpqouts114vs118_tagged.xls -fg -Vpq114vs118_infoFile.txt --tags="!out"
+sanxot.exe -apq114vs118_noOuts -p%workingFolder% -dsp114vs118_noOuts_higherLevel.tsv -rpqouts114vs118_tagged.tsv -fg -Vpq114vs118_infoFile.txt --tags="!out"
 REM see protein changes
-sanxot.exe -aqa114vs118 -p%workingFolder% -dpq114vs118_noOuts_higherLevel.xls -Cg
+sanxot.exe -aqa114vs118 -p%workingFolder% -dpq114vs118_noOuts_higherLevel.tsv -Cg
 </syntaxhighlight>