haystack_continuous_highD function

The main Haystack function, for higher-dimensional spaces and continuous expression levels.

haystack_continuous_highD(
  x,
  expression,
  grid.points = 100,
  weights.advanced.Q = NULL,
  dir.randomization = NULL,
  scale = TRUE,
  grid.method = "centroid",
  randomization.count = 100,
  n.genes.to.randomize = 100,
  selection.method.genes.to.randomize = "heavytails",
  grid.coord = NULL,
  spline.method = "ns"
)

Arguments

• x: Coordinates of cells in a 2D or higher-dimensional space. Rows represent cells, columns the dimensions of the space.
• expression: a matrix with expression data of genes (rows) in cells (columns)
• grid.points: An integer specifying the number of centers (grid points) to be used for estimating the density distributions of cells. Default is set to 100.
• weights.advanced.Q: (Default: NULL) Optional weights of cells for calculating a weighted distribution of expression.
• dir.randomization: If NULL, no output is made about the random sampling step. If not NULL, files related to the randomizations are printed to this directory.
• scale: Logical (default=TRUE) indicating whether input coordinates in x should be scaled to mean 0 and standard deviation 1.
• grid.method: The method to decide grid points for estimating the density in the high-dimensional space. Should be "centroid" (default) or "seeding".
• randomization.count: Number of randomizations to use. Default: 100
• n.genes.to.randomize: Number of genes to use in randomizations. Default: 100
• selection.method.genes.to.randomize: Method used to select genes for randomization.
• grid.coord: matrix of grid coordinates.
• spline.method: Method to use for fitting splines "ns" (default): natural splines, "bs": B-splines.

Returns

An object of class "haystack", including the results of the analysis, and the coordinates of the grid points used to estimate densities.

Examples

# using the toy example of the singleCellHaystack package

# running haystack
res <- haystack(dat.tsne, dat.expression)
# list top 10 biased genes
show_result_haystack(res, n=10)