From 0d1ea42abc7883634e146f0067b696c8a0df6ae6 Mon Sep 17 00:00:00 2001 From: drosofff Date: Thu, 7 Nov 2024 22:50:12 +0100 Subject: [PATCH] Update scran_normalize.xml --- tools/gsc_scran_normalize/scran_normalize.xml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tools/gsc_scran_normalize/scran_normalize.xml b/tools/gsc_scran_normalize/scran_normalize.xml index c00f70b7..5930aa6a 100644 --- a/tools/gsc_scran_normalize/scran_normalize.xml +++ b/tools/gsc_scran_normalize/scran_normalize.xml @@ -74,8 +74,10 @@ expression across the majority of genes represents some technical bias that shou Cell-specific biases are normalized using the computeSumFactors method, which implements the deconvolution strategy for scaling normalization (A. T. Lun, Bach, and Marioni 2016). It creates a reference : - - if no clustering step : the average count of all transcriptomes - - if you choose to cluster your cells : the average count of each cluster. + +- if no clustering step : the average count of all transcriptomes +- if you choose to cluster your cells : the average count of each cluster. + Then it pools cells and then sum their expression profiles. The size factor is described as the median ration between the count sums and the average across all genes. Finally it constructs a linear distribution (deconvolution method) of size factors by taking multiple pools of cells. @@ -83,9 +85,8 @@ of size factors by taking multiple pools of cells. You can apply this method on cell cluster instead of your all set of cells by using quickCluster. It defines cluster using distances based on Spearman correlation on counts between cells, there is two available methods : - - *hclust* : hierarchical clustering on the distance matrix and dynamic tree cut. - - *igraph* : constructs a Shared Nearest Neighbor graph (SNN) on the distance matrix and identifies highly connected communities. - +- *hclust* : hierarchical clustering on the distance matrix and dynamic tree cut. +- *igraph* : constructs a Shared Nearest Neighbor graph (SNN) on the distance matrix and identifies highly connected communities. Note: First header row must NOT start with a '#' comment character