| Next changeset 1:36fc0a87d7fb (2015-11-13) |
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abims_hclustering.r abims_hclustering.xml static/images/anova_filtered.png static/images/anova_pvalue.png static/images/hclust.png static/images/pca_abims_Rplots.png static/images/pca_abims_Rplots1.png static/images/pca_abims_eigenvalue.png static/images/pca_abims_percentage_of_variance.png |
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| diff -r 000000000000 -r 2f7381ee5235 abims_hclustering.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/abims_hclustering.r Tue Jun 30 06:36:09 2015 -0400 |
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| @@ -0,0 +1,38 @@ +#!/usr/local/public/bin/Rscript --verbose +# version="1.1" + +# date: 04-06-2013 +# **Authors** Gildas Le Corguille ABiMS - UPMC/CNRS - Station Biologique de Roscoff - gildas.lecorguille|at|sb-roscoff.fr + +# abims_hclust.r version 20130604 + +library(batch) +library(ctc) + +hclust_metabolomics = function(file, method = "pearson", link = "ward", normalization=TRUE, keep.hclust=FALSE, sep=";", dec="."){ + + if (sep=="tabulation") sep="\t" + if (sep=="semicolon") sep=";" + if (sep=="comma") sep="," + + # -- loading -- + data=read.table(file, header = TRUE, row.names=1, sep = sep, quote="\"", dec = dec, + fill = TRUE, comment.char="",na.strings = "NA") + + # -- Normalization: logratio -- + if (normalization) { + #meandata = apply(data,1,mean, na.rm=T) + #data = log2(data/meandata) + data=t(scale(t(data))) + } + + # -- hclust / output files for TreeView -- + file="hclust.cdt" + hclust2treeview(data,file=file, method = method, link = link, keep.hclust= keep.hclust) + + # -- output / return -- + system("zip -r hclust.zip hclust.*", ignore.stdout = TRUE) +} + +listArguments = parseCommandArgs(evaluate=FALSE) +do.call(hclust_metabolomics, listArguments) |
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| diff -r 000000000000 -r 2f7381ee5235 abims_hclustering.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/abims_hclustering.xml Tue Jun 30 06:36:09 2015 -0400 |
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| @@ -0,0 +1,192 @@ +<tool id="abims_hclustering" name="Hierarchical Clustering" version="1.1"> + + <description>using ctc R package for java-treeview</description> + + <command interpreter="Rscript"> + abims_hclustering.r file "$input" method $method link $link keep.hclust FALSE normalization $normalization sep "$sep" dec "$dec" + </command> + + <inputs> + <param name="input" type="data" label="Data Matrix file" format="tabular" help="Matrix of numeric data with headers." /> + <param name="method" type="select" label="Distance measure method" help="the distance measure to be used"> + <option value="pearson" selected="true">pearson</option> + <option value="euclidean" >euclidean</option> + <option value="maximum" >maximum</option> + <option value="manhattan" >manhattan</option> + <option value="canberra" >canberra</option> + <option value="binary" >binary</option> + <option value="correlation" >correlation</option> + <option value="spearman" >spearman</option> + </param> + <param name="link" type="select" label="Agglomeration/Link method" help="the agglomeration method to be used"> + <option value="ward" selected="true">ward</option> + <option value="single" >single</option> + <option value="complete" >complete</option> + <option value="average" >average</option> + <option value="mcquitty" >mcquitty</option> + <option value="median" >median</option> + <option value="centroid" >centroid</option> + </param> + <param name="normalization" type="select" label="Normalization by center and scale" help="Centering is done by subtracting the column means and scaling is done by dividing the (centered) columns of by their standard deviations"> + <option value="T" selected="true">TRUE</option> + <option value="F" >FALSE</option> + </param> + + <param name="sep" type="select" format="text" optional="true"> + <label>Separator of columns</label> + <option value="tabulation">tabulation</option> + <option value="semicolon">;</option> + <option value="comma">,</option> + </param> + <param name="dec" type="text" label="Decimal separator" value="." help="" /> + + <!--<param name="nr_col_names" type="integer" label="names" value="2" help="number of the column with names of metabolits" /> + <param name="from" type="integer" label="from" value="15" help="number of the column starting peak values data (to exlude all metadata)" /> + <param name="to" type="integer" label="to" value="30" help="number of the column finishing peak values data (to exlude all metadata)" /> + <param name="gr_number" type="integer" label="gr_number" value="2" help="number of groups (conditions)" /> + <param name="nb_col_gr" type="text" label="nb_col_gr" value="8,8" help="number of column of each group; separate with coma as indicated; first position coresponding to the first group etc." /> + <param name="threshold" type="float" label="threshold" value="0.01" help="max adjusted p.value accepted" />--> + + </inputs> + + <outputs> + <data name="hclust_zip" format="zip" from_work_dir="hclust.zip" label="${input.name[:-4]}.hclust.zip" /> + </outputs> + + <stdio> + <exit_code range="1:" level="fatal" /> + </stdio> + + <help> + + + +.. class:: infomark + +**Authors** Gildas Le Corguille ABiMS - UPMC/CNRS - Station Biologique de Roscoff - gildas.lecorguille|at|sb-roscoff.fr + +--------------------------------------------------- + +======================= +Hierarchical Clustering +======================= + +----------- +Description +----------- + +This function compute hierachical clustering with function +hcluster and export cluster to Java TreeView files format: jtreeview.sourceforge.net. + +This function performs a **hierarchical cluster analysis** using a set +of dissimilarities for the n objects being clustered. Initially, +each object is assigned to its own cluster and then the algorithm +proceeds iteratively, at each stage joining the two most similar +clusters, continuing until there is just a single cluster. At +each stage distances between clusters are recomputed by the +Lance-Williams dissimilarity update formula according to the +particular clustering method being used. + +A number of different **clustering methods** are provided. **Ward's** +minimum variance method aims at finding compact, spherical +clusters. The **complete linkage** method finds similar clusters. +The **single linkage** method (which is closely related to the +minimal spanning tree) adopts a ‘friends of friends’ clustering +strategy. The other methods can be regarded as aiming for +clusters with characteristics somewhere between the single and +complete link methods. Note however, that methods **median** and +**centroid** are not leading to a monotone distance measure, +or equivalently the resulting dendrograms can have so called +inversions (which are hard to interpret). + + + + +----------- +Input files +----------- + ++---------------------------+------------+ +| Parameter : num + label | Format | ++===========================+============+ +| 1 : Data Matrix file | Tabular | ++---------------------------+------------+ + + +---------- +Parameters +---------- + + +**Agglomeration or Link method:* + +A number of different clustering methods are provided. Ward's minimum variance method aims at finding compact, spherical clusters. +The complete linkage method finds similar clusters. The single linkage method (which is closely related to the minimal spanning tree) adopts a ‘friends of friends’ clustering strategy. +The other methods can be regarded as aiming for clusters with characteristics somewhere between the single and complete link methods. +Note however, that methods median and centroid are not leading to a monotone distance measure, or equivalently the resulting dendrograms can have so called inversions (which are hard to interpret). + + +------------ +Output files +------------ + +***.tab.hclust.zip** + + | A zip file containing three files (hclust.atr, hclust.cdt and hclust.gtr) that are Treeview format. If you want to have more informations or download Treeview, you can visit the webiste: + | http://jtreeview.sourceforge.net + + + +------ + +.. class:: infomark + +You can continue your analysis using Treeview (outside of Galaxy) with the three files (atr,cdt and gtr) within the **xset.tab.hclust.zip** output. + + + + +--------------------------------------------------- + +--------------- +Working example +--------------- + + +Input files +----------- + +**>A part of an example of Data Matrix file input** + + ++--------+------------------+----------------+ +| Name | Bur-eH_FSP_102 | Bur-eH_FSP_22 | ++========+==================+================+ +|M202T601| 91206595.7559783 |106808979.08546 | ++--------+------------------+----------------+ +|M234T851| 27249137.275504 |28824971.3177926| ++--------+------------------+----------------+ + + +Parameters +---------- + + | Distance measure method -> **pearson** + | Agglomeration/Link method -> **ward** + | Normalization by center and scale -> **TRUE** + | Separator of columns -> **tabulation** + | Decimal separator: -> **.** + + + +Output files +------------ + +**Example of an dendrogram/heatmap generated by the Treeview tool**: + +.. image:: hclust.png + + + </help> + +</tool> |
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