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De noising Filters
Brian Haas edited this page Mar 11, 2019
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These de-noising filter options are available for manipulating the residual expression intensities with the goal of reducing the noise (residual signal in the normal cells) while retaining the signal in tumor cells that could be interpreted as supporting CNV.
The residual normal signal is derived from the preliminary inferCNV object, which has been smoothed, centered, and the mean of the normal (reference) cells subtracted:
A specific threshold deviation from the mean can be set using the 'noise_filter' attribute, as shown below:
infercnv_obj = infercnv::run(infercnv_obj,
cutoff=1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
out_dir=out_dir,
cluster_by_groups=T,
plot_steps=F,
denoise=T,
noise_filter=0.1 ## hard thresholds
)
- 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