global.velcoity.estimates: Structure-based gene velocity estimation
In sqjin/nlvelo: RNA velocity estimation using nonlinear models

Description Usage Arguments Value Examples

Structure-based gene velocity estimation

global.velcoity.estimates(
  emat,
  nmat,
  vel,
  base.df,
  deltaT = 1,
  smat = NULL,
  kGenes = 15,
  kGenes.trim = 5,
  smooth.kGenes = 0,
  kCells = 10,
  deltaT2 = 1,
  min.gene.conuts = 100,
  min.gene.cells = 20,
  min.intron.length = 10^3.5,
  min.exon.length = 10^2.7,
  top.global.pearson.deviance = 3,
  cellKNN = NULL,
  cell.dist = NULL,
  fit.quantile = NULL,
  zero.offset = NULL,
  diagonal.quantiles = FALSE,
  m.pcount = 5,
  plot.model.fit = FALSE,
  n.cores = defaultNCores()
)

`emat`	- spliced (exonic) count matrix
`nmat`	- unspliced (nascent) count matrix
`vel`	- initial gene-relative velocity estimates (output of the gene.relative.velocity.estimates function)
`base.df`	gene structure information data frame ($gene.df in output of read.gene.mapping.info()), containing the following columns ($il - total intronic length in log10(length+1) scale; $el - total exonic length; $nex - number of expressed (above some low threshold) exons; as well as optional $nipconc/$nipdisc giving number of concordant and discordant internal priming sites)
`deltaT`	- amount of time to project the cell forward
`smat`	- optional spanning read matrix (used in offset calculations)
`kGenes`	- number of genes to use in evaluating trimmed mean of M values
`kGenes.trim`	- number of genes to trim (from both ends)
`smooth.kGenes`	- gene kNN pooling k value (used in the initial gene-relative fit)
`kCells`	- number of k nearest neighbors (NN) to use in slope calculation smoothing
`deltaT2`	- scaling of the projected difference vector (normally should be set to 1)
`min.gene.conuts`	- minimum number of spliced reads/molecules that a gene should have
`min.gene.cells`	- minimum number of cells in which a gene should be expressed
`min.intron.length`	- minimum exon length
`min.exon.length`	- minimum exon length
`top.global.pearson.deviance`	- maximum deviance threshold to filter out genes with very high unsplied counts (likely due to other processes)
`cellKNN`	- optional pre-calculated cell KNN matrix
`cell.dist`	- cell distance to use in cell kNN pooling calculations
`fit.quantile`	perform gamma fit on a top/bottom quantiles of expression magnitudes
`zero.offset`	force gene offsets to be zero (default if smat is not supplied), otherwise estimated from the lower quantile or quantile fit
`diagonal.quantiles`	whether diagonal quantile determination should be used (if fit.quantile is specified)
`m.pcount`	- pseudocount to be used in M value calculations (defaults to 5)
`plot.model.fit`	plot gamma values predicted by the structure-bsaed model as a function of gene-relative gamma estimates.
`n.cores`	- number of cores to use

a list with velocity results, including the current normalized expression state ($current), projected ($projected), unscaled transcriptional change ($deltaE), fit results ($ko, $sfit), optional cell pooling parameters ($cellKNN, $kCells), kNN-convolved normalized matrices (conv.nmat.norm and conv.emat.norm)

## Not run: 
 # emat / nmat are the spliced/unpsliced matrices respectively
 # rvel is a gene-relative velocity estimate
 # base.df (here dat$base.df) is a gene information table.
 #   For SMART-seq2, it is part of the \code{\link{read.smartseq2.bams}} output.
 #   For droplet data, this info can be obtained \code{\link{}}
 gvel <- global.velcoity.estimates(emat, nmat, rvel, dat$base.df, deltaT=1, kCells=5,
       kGenes = 15, kGenes.trim = 5, min.gene.cells = 0, min.gene.conuts = 500)


## End(Not run)