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Smoothing_methods
GeorgescuC edited this page Aug 23, 2019
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Each method is applied on individual cells' data, and independently per chromosome.
Simple mean over the genes within (window_size-1)/2 on each side.
Weighted mean over the genes within (window_size-1)/2 on each side. With n = (window_size-1)/2
, the weight for the m-th gene in the neighborhood of a given gene is (n+1-m)/(n+1)
This feature is only available from version 1.1.3 onward. Weighted mean over the genes within window_size base pairs on each side of a given gene. The weight for a gene with their center at m base pairs from the current gene's center is 1-(m/window_size). This method can give the "cleanest" limits for CNVs, when there is a large gap around it among the expressed genes.
- InferCNV Home
- Quick Start
- Installing inferCNV
- Running InferCNV
- Applying Noise Filters
- Predicting CNV via HMM
- Bayesian Mixture Model
- Tumor heterogeneity - define tumor subclusters
- Interpreting the Figure
- Inputs to InferCNV
- Outputs from InferCNV
- More inferCNV example data sets
- Using 10x data
- Interactively navigating data using the Next Generation Heatmap Viewer
- Extracting HMM features
- FAQ and common issues