De noising Filters

inferCNV de-noising filters

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:

Filtering using defined thresholds:

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
                             )

Filtering using Dynamic thresholding (default setting)

By default, the hard cutoffs for denoising are computed based on the standard deviation of the residual normal expression values. This thresholding can be adjusted using the 'sd_amplifier' setting. For example, we can use 1.5 * the standard deviation for filtering like so:

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,
                             sd_amplifier=1.5  ## set dynamic thresholding based on the standard deviation value.
                             )

Adjusting intensities via sigmoidal function (logistic)

Instead of applying a strict threshold, you can apply a filtering gradient by applying a sigmoidal function that reduces intensities near the mean more than intensities more distant from the mean. An example of applying this 'noise_logistic' is 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,
                             sd_amplifier=3,
                             noise_logistic=TRUE
                             )