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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_scran_normalize commit 9ab82433f375b37be5c9acb22e5deb798081dc3b
author artbio
date Thu, 07 Nov 2024 22:02:01 +0000
parents 6864acb21714
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2:6864acb21714 3:cc768b0f41cf
1 <tool id="scran_normalize" name="scran_normalize" version="1.28.1+galaxy0"> 1 <tool id="scran_normalize" name="scran_normalize" version="1.28.1+galaxy1">
2 <description>Normalize raw counts expression values using deconvolution size factors</description> 2 <description>Normalize raw counts expression values using deconvolution size factors</description>
3 <xrefs>
4 <xref type="bio.tools">galaxy_single_cell_suite</xref>
5 </xrefs>
3 <requirements> 6 <requirements>
4 <requirement type="package" version="1.28.1">bioconductor-scran</requirement> 7 <requirement type="package" version="1.28.1">bioconductor-scran</requirement>
5 <requirement type="package" version="1.63_1">r-dynamictreecut</requirement> 8 <requirement type="package" version="1.63_1">r-dynamictreecut</requirement>
6 <requirement type="package" version="1.7.3">r-optparse</requirement> 9 <requirement type="package" version="1.7.3">r-optparse</requirement>
7 </requirements> 10 </requirements>
69 most genes are not differentially expressed (DE) between cells, such that any differences in 72 most genes are not differentially expressed (DE) between cells, such that any differences in
70 expression across the majority of genes represents some technical bias that should be removed. 73 expression across the majority of genes represents some technical bias that should be removed.
71 74
72 Cell-specific biases are normalized using the computeSumFactors method, which implements the 75 Cell-specific biases are normalized using the computeSumFactors method, which implements the
73 deconvolution strategy for scaling normalization (A. T. Lun, Bach, and Marioni 2016). It creates a reference : 76 deconvolution strategy for scaling normalization (A. T. Lun, Bach, and Marioni 2016). It creates a reference :
74 - if no clustering step : the average count of all transcriptomes 77
75 - if you choose to cluster your cells : the average count of each cluster. 78 - if no clustering step : the average count of all transcriptomes
79 - if you choose to cluster your cells : the average count of each cluster.
80
76 Then it pools cells and then sum their expression profiles. The size factor is described as the median ration 81 Then it pools cells and then sum their expression profiles. The size factor is described as the median ration
77 between the count sums and the average across all genes. Finally it constructs a linear distribution (deconvolution method) 82 between the count sums and the average across all genes. Finally it constructs a linear distribution (deconvolution method)
78 of size factors by taking multiple pools of cells. 83 of size factors by taking multiple pools of cells.
79 84
80 You can apply this method on cell cluster instead of your all set of cells by using quickCluster. 85 You can apply this method on cell cluster instead of your all set of cells by using quickCluster.
81 It defines cluster using distances based on Spearman correlation on counts between cells, there is two available methods : 86 It defines cluster using distances based on Spearman correlation on counts between cells, there is two available methods :
82 87
83 - *hclust* : hierarchical clustering on the distance matrix and dynamic tree cut. 88 - *hclust* : hierarchical clustering on the distance matrix and dynamic tree cut.
84 - *igraph* : constructs a Shared Nearest Neighbor graph (SNN) on the distance matrix and identifies highly connected communities. 89 - *igraph* : constructs a Shared Nearest Neighbor graph (SNN) on the distance matrix and identifies highly connected communities.
85
86 90
87 Note: First header row must NOT start with a '#' comment character 91 Note: First header row must NOT start with a '#' comment character
88 92
89 </help> 93 </help>
90 <citations> 94 <citations>