calcCNV: Calculate CNV scores given a matrix of RNASeq counts. Output...
In CharleneZ95/infercnvPlus: Enhanced 'infercnv' package

Calculate CNV scores given a matrix of RNASeq counts. Output a matrix of final values.

calcCNV(
  data,
  gene_pos,
  cutoff,
  reference_obs,
  window_size,
  out_path,
  contig_tail,
  noise_filter,
  vis_bounds
)

`data:`	expression matrix (genes X cells), assumed to be log2(TPM+1) .
`gene_order:`	ordering of the genes (data's rows) according to their genomic location To include all genes use 0.
`cutoff:`	cut-off for the average expression of genes to be used for CNV inference.
`reference_obs:`	Column names of the subset of cells (data's columns) that should be used as references. If not given, the average of all cells will be the reference.
`window_size:`	length of the window for the moving average (smoothing). Should be an odd integer.
`out_path:`	the path to what to save the pdf as. The raw data is also written to this path but with the extension .txt .
`contig_tail:`	length of the tail removed from the ends of contigs.
`noise_filter:`	the minimum difference a value can be from the average reference in order for it not to be removed as noise.
`vis_bounds:`	Used as upper and lower bounds for values in the visualization. Should be given in the form of '-1,1' (lower bound, upper bound).

Returns an 'infercnv' object including: 1. CNV matrix before visualization. 2. CNV matrix after denoise and outlier removal for visualization. 3. Chromosome order. 4. Names of cells in reference groups.

CharleneZ95/infercnvPlus documentation built on April 9, 2020, 3:40 a.m.