| Previous changeset 148:1e20061decdd (2015-04-29) |
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Commit message:
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/differential_count_models commit 344140b8df53b8b7024618bb04594607a045c03a |
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modified:
rgedgeRpaired_nocamera.xml |
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| diff -r 1e20061decdd -r 3107df74056e rgedgeRpaired_nocamera.xml --- a/rgedgeRpaired_nocamera.xml Wed Apr 29 12:07:19 2015 -0400 +++ b/rgedgeRpaired_nocamera.xml Mon May 04 22:47:36 2015 -0400 |
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| b'@@ -7,119 +7,16 @@\n <requirement type="package" version="9.10">ghostscript</requirement>\n <requirement type="package" version="2.14">biocbasics</requirement>\n </requirements>\n- <command interpreter="python">\n- rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "Differential_Counts" \n- --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"\n- </command>\n- <inputs>\n- <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample" help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>\n- <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain">\n- <sanitizer invalid_char="">\n- <valid initial="string.letters,string.digits">\n- <add value="_"/>\n- </valid>\n- </sanitizer>\n- </param>\n- <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>\n- <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes" force_select="True">\n- <validator type="no_options" message="Please select at least one column."/>\n- </param>\n- <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>\n- <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" multiple="true" use_header_names="true" size="120" display="checkboxes" force_select="True">\n- </param>\n- <param name="subjectids" type="text" optional="true" size="120" value="" label="IF SUBJECTS NOT ALL INDEPENDENT! Enter comma separated strings to indicate sample labels for (eg) pairing - must be one for every column in input" help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter \'A99,C21,A99,C21\'">\n- <sanitizer>\n- <valid initial="string.letters,string.digits">\n- <add value=","/>\n- </valid>\n- </sanitizer>\n- </param>\n- <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs" help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/>\n- <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples" help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/>\n- <conditional name="edgeR">\n- <param name="doedgeR" type="select" label="Run this model using edgeR" help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates">\n- <option value="F">Do not run edgeR</option>\n- <option value="T" selected="true">Run edgeR</option>\n- </param>\n- <when value="T">\n- <param name="edgeR_priordf" type="integer" value="10" size="3" label="prior.df for tagwise dispersion - larger value = more squeezing of tag dispersions to common dispersion. Replaces prior.n and prior.df = prior.n * residual.df" help="10 = edgeR default. Use a larger value to \'smooth\' small samples. See edgeR docs and note below"/>\n- <param name="edgeR_robust_method" type="select" value="20" size="3" label="Use robust dispersion method" help="Use ordinary, anscombe or deviance robust deviance estimates">\n- <option value="ordinary" selected="true">Use ordinary deviance estimates</opt'..b' (the version for R 2.14 as at the time of writing). \n-This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing \n+The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue)\n+breaking this and all other code that assumed the old name for this variable,\n+between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing).\n+This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing\n to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly\n when their old scripts break. This tool currently now works with 2.4.6.\n \n@@ -974,19 +974,19 @@\n \n *prior.n*\n \n-The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. \n-You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood \n-in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your \n-tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the \n+The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion.\n+You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood\n+in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your\n+tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the\n common likelihood the weight of one observation.\n \n-In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, \n-or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that \n-you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation \n-(squeezing) of the tagwise dispersions. How many samples do you have in your experiment? \n-What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10. \n-If you have more samples, then the tagwise dispersion estimates will be more reliable, \n-so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. \n+In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value,\n+or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that\n+you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation\n+(squeezing) of the tagwise dispersions. How many samples do you have in your experiment?\n+What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10.\n+If you have more samples, then the tagwise dispersion estimates will be more reliable,\n+so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5.\n \n \n From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:\n@@ -1023,17 +1023,17 @@\n \n **Attributions**\n \n-edgeR - edgeR_ \n+edgeR - edgeR_\n \n-VOOM/limma - limma_VOOM_ \n+VOOM/limma - limma_VOOM_\n \n DESeq2 - DESeq2_ for details\n \n See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package.\n \n-Galaxy_ (that\'s what you are using right now!) for gluing everything together \n+Galaxy_ (that\'s what you are using right now!) for gluing everything together\n \n-Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is \n+Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is\n licensed to you under the LGPL_ like other rgenetics artefacts\n \n .. _LGPL: http://www.gnu.org/copyleft/lesser.html\n' |