Mercurial > repos > iuc > schicexplorer_schicclusterminhash
diff scHicClusterMinHash.xml @ 1:68648299ffc4 draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/schicexplorer commit 72e1e90ac05a32dbd6fc675073429c0086048b18"
author | iuc |
---|---|
date | Tue, 10 Mar 2020 15:11:23 -0400 |
parents | 1c2e79e9311a |
children | 3048283ee054 |
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--- a/scHicClusterMinHash.xml Thu Jan 23 16:04:57 2020 -0500 +++ b/scHicClusterMinHash.xml Tue Mar 10 15:11:23 2020 -0400 @@ -8,7 +8,7 @@ <command detect_errors="exit_code"><![CDATA[ @BINARY@ - --matrix '$matrix_mcooler' + --matrix '$matrix_scooler' --numberOfClusters $numberOfClusters @@ -24,6 +24,7 @@ #end if --outFileName cluster_list.txt + --numberOfNearestNeighbors $numberOfNearestNeighbors --threads @THREADS@ @@ -32,7 +33,7 @@ ]]></command> <inputs> - <expand macro="matrix_mcooler_macro"/> + <expand macro="matrix_scooler_macro"/> <param name="clusterMethod_selector" type="select" label="Cluster method:"> <option value="kmeans" selected="True">K-means</option> <option value="spectral" >Spectral clustering</option> @@ -40,6 +41,7 @@ <param name="numberOfClusters" type="integer" value="7" label="Number of clusters" help='How many clusters should be computed by kmeans or spectral clustering' /> <param name="numberOfHashFunctions" type="integer" value="800" label="Number of hash functions" help='How many hash functions the minHash algorithm uses.' /> + <param name="numberOfNearestNeighbors" type="integer" value="100" label="Number of nearest neighbors" help='How many nearest neighbors should be computed for the k-nn graph?' /> <param name='chromosomes' type='text' label='List of chromosomes to consider' help='Please separate the chromosomes by space'/> <param name='exactModeMinhash' type='boolean' truevalue='--exactModeMinHash' label='The MinHash algorithm computes additional the exact euclidean distance.'/> @@ -50,7 +52,7 @@ </outputs> <tests> <test> - <param name='matrix_mcooler' value='test_matrix.mcool' /> + <param name='matrix_scooler' value='test_matrix.scool' /> <param name='clusterMethod_selector' value='kmeans' /> <param name='numberOfClusters' value='3' /> <param name='numberOfHashFunctions' value='800' /> @@ -58,7 +60,7 @@ <output name="outFileName" file="scHicClusterMinHash/cluster_kmeans.txt" ftype="txt" compare="sim_size" delta="4000"/> </test> <test> - <param name='matrix_mcooler' value='test_matrix.mcool' /> + <param name='matrix_scooler' value='test_matrix.scool' /> <param name='clusterMethod_selector' value='spectral' /> <param name='numberOfClusters' value='3' /> <param name='numberOfHashFunctions' value='800' /> @@ -66,7 +68,7 @@ <output name="outFileName" file="scHicClusterMinHash/cluster_spectral.txt" ftype="txt" compare="sim_size" delta="4000"/> </test> <test> - <param name='matrix_mcooler' value='test_matrix.mcool' /> + <param name='matrix_scooler' value='test_matrix.scool' /> <param name='clusterMethod_selector' value='kmeans' /> <param name='numberOfClusters' value='3' /> <param name='numberOfHashFunctions' value='800' /> @@ -75,7 +77,7 @@ <output name="outFileName" file="scHicClusterMinHash/cluster_kmeans_exact.txt" ftype="txt" compare="sim_size" delta="4000"/> </test> <test> - <param name='matrix_mcooler' value='test_matrix.mcool' /> + <param name='matrix_scooler' value='test_matrix.scool' /> <param name='clusterMethod_selector' value='spectral' /> <param name='numberOfClusters' value='3' /> <param name='numberOfHashFunctions' value='800' /> @@ -92,10 +94,10 @@ Clustering with dimension reduction via MinHash =============================================== -scHicClusterMinHash uses kmeans or spectral clustering to associate each cell to a cluster and therefore to its cell cycle. -The clustering is applied on dimension reduced data based on an approximate kNN search with the local sensitive hashing technique MinHash. This approach reduces the number of dimensions from samples * (number of bins)^2 to samples * samples. -Please consider also the other clustering and dimension reduction approaches of the scHicExplorer suite. They can give you better results, -can be faster or less memory demanding. +scHicClusterMinHash uses kmeans or spectral clustering to associate each cell to a cluster and therefore to its cell cycle. +The clustering is applied on dimension reduced data based on an approximate kNN search with the local sensitive hashing technique MinHash. This approach reduces the number of dimensions from samples * (number of bins)^2 to samples * samples. The clustering is applied on dimension reduced data based on an approximate kNN search with the local sensitive hashing technique MinHash. This approach reduces the number of dimensions from samples * (number of bins)^2 to samples * samples. +Please consider also the other clustering and dimension reduction approaches of the scHicExplorer suite such as `scHicCluster`, `scHicClusterMinHash` and `scHicClusterSVL`. They can give you better results, Please consider also the other clustering and dimension reduction approaches of the scHicExplorer suite such as `scHicCluster`, `scHicClusterCompartments` and `scHicClusterSVL`. They can give you better results, +can be faster or less memory demanding. can be faster or less memory demanding. For more information about scHiCExplorer please consider our documentation on readthedocs.io_