diff transit_resampling.xml @ 2:335197e8f75e draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/transit/ commit f63413d629e4de3c69984b3a96ad8ccfe0d47ada"

--- a/transit_resampling.xml	Tue Jun 25 17:44:35 2019 -0400
+++ b/transit_resampling.xml	Tue Oct 08 08:23:54 2019 -0400
@@ -13,16 +13,13 @@
         #end for
         transit resampling $input_files $control_files annotation.dat transit_out.txt
         @STANDARD_OPTIONS@
-        -s $samples -n $normalization $histogram $adaptive $exclude_zero $pseudo $loess
+        -s $samples $histogram $adaptive $exclude_zero $pseudo $loess
         ]]>
     </command>
     <inputs>
         <expand macro="standard_inputs">
             <param name="controls" type="data" format="wig" multiple="true" label="Control .wig files" />
             <param name="samples" argument="-s" type="integer" value="10000" label="Number of samples" />
-            <param name="normalization" argument="-n" type="select" label="Normalization method">
-                <option value="TTR">TTR</option>
-            </param>
             <param name="histogram" argument="-h" type="boolean" truevalue="-h" falsevalue="" label="Output histogram of the permutations" />
             <param name="adaptive" argument="-a" type="boolean" truevalue="-a" falsevalue="" label="Perform adaptive resampling" />
             <param name="exclude_zero" argument="-ez" type="boolean" truevalue="-ez" falsevalue="" label="Exclude rows with zero accross conditions" />
@@ -38,7 +35,7 @@
         <test>
             <param name="inputs" ftype="wig" value="transit-in1-rep1.wig,transit-in1-rep2.wig" />
             <param name="controls" ftype="wig" value="transit-co1-rep1.wig,transit-co1-rep2.wig,transit-co1-rep3.wig" />
-            <param name="annotation" ftype="gff3" value="transit-in1.gff3" />
+            <param name="annotation" ftype="tabular" value="transit-in1.prot" />
             <param name="samples" value="1000" />
             <param name="burnin" value="100" />
             <param name="replicates" value="Replicates" />
@@ -102,6 +99,13 @@
 -   Adaptive Resampling: An optional “adaptive” version of resampling which accelerates the calculation by terminating early for genes which are likely not significant. This dramatically speeds up the computation at the cost of less accurate estimates for those genes that terminate early (i.e. deemed not significant). This option is OFF by default.
 -   Include Zeros: Select to include sites that are zero. This is the preferred behavior, however, unselecting this (thus ignoring sites that) are zero accross all dataset (i.e. completely empty), is useful for decreasing running time (specially for large datasets like Tn5).
 -   Normalization Method: Determines which normalization method to use when comparing datasets. Proper normalization is important as it ensures that other sources of variability are not mistakenly treated as real differences. See the Normalization section for a description of normalization method available in TRANSIT.
+    - TTR (Default) : Trimmed Total Reads (TTR), normalized by the total read-counts (like totreads), but trims top and bottom 5% of read-counts. This is the recommended normalization method for most cases as it has the beneffit of normalizing for difference in saturation in the context of resampling.
+    - nzmean : Normalizes datasets to have the same mean over the non-zero sites.
+    - totreads : Normalizes datasets by total read-counts, and scales them to have the same mean over all counts.
+    - zinfnb : Fits a zero-inflated negative binomial model, and then divides read-counts by the mean. The zero-inflated negative binomial model will treat some empty sites as belonging to the “true” negative binomial distribution responsible for read-counts while treating the others as “essential” (and thus not influencing its parameters).
+    - quantile : Normalizes datasets using the quantile normalization method described by Bolstad et al. (2003). In this normalization procedure, datasets are sorted, an empirical distribution is estimated as the mean across the sorted datasets at each site, and then the original (unsorted) datasets are assigned values from the empirical distribution based on their quantiles.
+    - betageom : Normalizes the datasets to fit an “ideal” Geometric distribution with a variable probability parameter p. Specially useful for datasets that contain a large skew. See Beta-Geometric Correction .
+    - nonorm : No normalization is performed.
 
 
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