gene.relative.velocity.estimates: Estimate RNA velocity using gene-relative slopes
In sqjin/nlvelo: RNA velocity estimation using nonlinear models

Description Usage Arguments Value Examples

Estimate RNA velocity using gene-relative slopes

gene.relative.velocity.estimates(
  emat,
  nmat,
  deltaT = 1,
  smat = NULL,
  steady.state.cells = colnames(emat),
  kCells = 10,
  cellKNN = NULL,
  kGenes = 1,
  old.fit = NULL,
  mult = 1000,
  min.nmat.smat.correlation = 0.05,
  min.nmat.emat.correlation = 0.05,
  min.nmat.emat.slope = 0.05,
  zero.offset = FALSE,
  deltaT2 = 1,
  fit.quantile = NULL,
  diagonal.quantiles = FALSE,
  show.gene = NULL,
  do.par = TRUE,
  cell.dist = NULL,
  emat.size = NULL,
  nmat.size = NULL,
  cell.emb = NULL,
  cell.colors = NULL,
  expression.gradient = NULL,
  residual.gradient = NULL,
  K = 0.5,
  N = 1,
  n.cores = defaultNCores(),
  verbose = TRUE
)

`emat`	- spliced (exonic) count matrix
`nmat`	- unspliced (nascent) count matrix
`deltaT`	- amount of time to project the cell forward
`smat`	- optional spanning read matrix (used in offset calculations)
`steady.state.cells`	- optional set of steady-state cells on which the gamma should be estimated (defaults to all cells)
`kCells`	- number of k nearest neighbors (NN) to use in slope calculation smoothing
`cellKNN`	- optional pre-calculated cell KNN matrix
`kGenes`	- number of genes (k) to use in gene kNN pooling
`old.fit`	- optional old result (in this case the slopes and offsets won't be recalculated, and the same kNN graphs will be used)
`mult`	- library scaling factor (1e6 in case of FPM)
`min.nmat.smat.correlation`	- minimum required Spearman rank correlation between n and s counts of a gene
`min.nmat.emat.correlation`	- minimum required Spearman rank correlation between n and e counts of a gene
`min.nmat.emat.slope`	- minimum sloope of n~e regression
`zero.offset`	- should offset be set to zero, or determined (through smat regression or using near-0 e cases)
`deltaT2`	- scaling of the projected difference vector (normally should be set to 1)
`fit.quantile`	perform gamma fit on a top/bottom quantiles of expression magnitudes
`diagonal.quantiles`	whether extreme quantiles should be computed diagonally
`show.gene`	an optional name of a gene for which the velocity estimation details should be shown (instead of estimating all velocities)
`do.par`	whether the graphical device parameters should be reset as part of show.gene (default=TRUE)
`cell.dist`	- cell distance to use in cell kNN pooling calculations
`emat.size`	- pre-calculated cell sizes for the emat (spliced) matrix
`nmat.size`	- pre-calculated cell sizes for the nmat (unspliced) matrix
`cell.emb`	- cell embedding to be used in show.gene function
`cell.colors`	- cell colors to be used in show.gene function
`expression.gradient`	- color palette used to show the expression magnitudes in show.gene function
`residual.gradient`	- color palette used to show the u residuals in show.gene function
`K`	- a constant in the Hill function
`N`	- Hill coefficient in modeling the nonlinear relationship
`n.cores`	- number of cores to use
`verbose`	- output messages about progress

a list with velocity results, including the current normalized expression state ($current), projected ($projected) over a certain time ($deltaT), unscaled transcriptional change ($deltaE), fit results ($gamma, $ko, $sfit if spanning reads were used), optional cell pooling parameters ($cellKNN, $kCells), kNN-convolved normalized matrices (conv.nmat.norm and conv.emat.norm), library scale ($mult)

## Not run: 
 # use min/max quantile gamma fit (recommended option when one can afford to do cell kNN smoothing)
 # The example below uses k=5 cell kNN pooling, and top/bottom 2% exprssion quantiles
 # emat and nmat are spliced (exonic) and unspliced (intronic) molecule/read count matirces
(preferably filtered for informative genes)
 rvel <- gene.relative.velocity.estimates(emat,nmat,deltaT=1,kCells = 5,fit.quantile = 0.02)

 # alternativly, the function can be used to visualize gamma fit and regression for a
particular gene. here we pass embedding (a matrix/data frame with rows named with cell names,
and columns corresponding to the x/y coordinates)

 # and cell colors. old.fit is used to save calculation time.
 gene.relative.velocity.estimates(emat,nmat,deltaT=1,kCells = 5,fit.quantile = 0.02,
    old.fit=rvel,show.gene='Chga',cell.emb=emb,cell.colors=cell.colors)

## End(Not run)