velocyto.estimation module

velocyto.estimation.colDeltaCor(emat: numpy.ndarray, dmat: numpy.ndarray, threads: int = None) → numpy.ndarray[source]

Calculate the correlation between the displacement (d[:,i]) and the difference between a cell and every other (e - e[:, i])

Parallel cython+OpenMP implemetation

Parameters:
  • emat (np.ndarray (ngenes, ncells)) – gene expression matrix
  • dmat (np.ndarray (ngenes, ncells)) – gene velocity/displacement matrix
  • threads (int) – number of parallel threads to use
velocyto.estimation.colDeltaCorpartial(emat: numpy.ndarray, dmat: numpy.ndarray, ixs: numpy.ndarray, threads: int = None) → numpy.ndarray[source]

Calculate the correlation between the displacement (d[:,i]) and the difference between a cell and every other (e - e[:, i])

Parallel cython+OpenMP implemetation

Parameters:
  • emat (np.ndarray (ngenes, ncells)) – gene expression matrix
  • dmat (np.ndarray (ngenes, ncells)) – gene velocity/displacement matrix
  • ixs (the neighborhood matrix (ncells, nneighbours)) – ixs[i, k] is the kth neighbour to the cell i
  • threads (int) – number of parallel threads to use
velocyto.estimation.colDeltaCorLog10(emat: numpy.ndarray, dmat: numpy.ndarray, threads: int = None, psc: float = 1.0) → numpy.ndarray[source]

Calculate the correlation between the displacement (d[:,i]) and the difference between a cell and every other (e - e[:, i])

Parallel cython+OpenMP implemetation

Parameters:
  • emat (np.ndarray (ngenes, ncells)) – gene expression matrix
  • dmat (np.ndarray (ngenes, ncells)) – gene velocity/displacement matrix
  • threads (int) – number of parallel threads to use
velocyto.estimation.colDeltaCorLog10partial(emat: numpy.ndarray, dmat: numpy.ndarray, ixs: numpy.ndarray, threads: int = None, psc: float = 1.0) → numpy.ndarray[source]

Calculate the correlation between the displacement (d[:,i]) and the difference between a cell and every other (e - e[:, i])

Parallel cython+OpenMP implemetation

Parameters:
  • emat (np.ndarray (ngenes, ncells)) – gene expression matrix
  • dmat (np.ndarray (ngenes, ncells)) – gene velocity/displacement matrix
  • ixs (the neighborhood matrix (ncells, nneighbours)) – ixs[i, k] is the kth neighbour to the cell i
  • threads (int) – number of parallel threads to use
velocyto.estimation.colDeltaCorSqrt(emat: numpy.ndarray, dmat: numpy.ndarray, threads: int = None, psc: float = 0.0) → numpy.ndarray[source]

Calculate the correlation between the displacement (d[:,i]) and the difference between a cell and every other (e - e[:, i])

Parallel cython+OpenMP implemetation

Parameters:
  • emat (np.ndarray (ngenes, ncells)) – gene expression matrix
  • dmat (np.ndarray (ngenes, ncells)) – gene velocity/displacement matrix
  • threads (int) – number of parallel threads to use
velocyto.estimation.colDeltaCorSqrtpartial(emat: numpy.ndarray, dmat: numpy.ndarray, ixs: numpy.ndarray, threads: int = None, psc: float = 0.0) → numpy.ndarray[source]

Calculate the correlation between the displacement (d[:,i]) and the difference between a cell and every other (e - e[:, i])

Parallel cython+OpenMP implemetation

Parameters:
  • emat (np.ndarray (ngenes, ncells)) – gene expression matrix
  • dmat (np.ndarray (ngenes, ncells)) – gene velocity/displacement matrix
  • ixs (the neighborhood matrix (ncells, nneighbours)) – ixs[i, k] is the kth neighbour to the cell i
  • threads (int) – number of parallel threads to use
velocyto.estimation.fit_slope(Y: numpy.ndarray, X: numpy.ndarray) → numpy.ndarray[source]

Loop through the genes and fits the slope

Y: np.ndarray, shape=(genes, cells)
the dependent variable (unspliced)
X: np.ndarray, shape=(genes, cells)
the independent variable (spliced)
velocyto.estimation.fit_slope_offset(Y: numpy.ndarray, X: numpy.ndarray, fixperc_q: bool = False) → typing.Tuple[numpy.ndarray, numpy.ndarray][source]

Loop through the genes and fits the slope

Y: np.ndarray, shape=(genes, cells)
the dependent variable (unspliced)
X: np.ndarray, shape=(genes, cells)
the independent variable (spliced)
velocyto.estimation.fit_slope_weighted(Y: numpy.ndarray, X: numpy.ndarray, W: numpy.ndarray, bounds: typing.Tuple[float, float] = (0, 3)) → numpy.ndarray[source]

Loop through the genes and fits the slope

Y: np.ndarray, shape=(genes, cells)
the dependent variable (unspliced)
X: np.ndarray, shape=(genes, cells)
the independent variable (spliced)
W: np.ndarray, shape=(genes, cells)
the weights that will scale the square residuals
velocyto.estimation.fit_slope_weighted_offset(Y: numpy.ndarray, X: numpy.ndarray, W: numpy.ndarray, fixperc_q: bool = False, return_R2: bool = True, limit_gamma: bool = False) → typing.Any[source]

Loop through the genes and fits the slope

Y: np.ndarray, shape=(genes, cells)
the dependent variable (unspliced)
X: np.ndarray, shape=(genes, cells)
the independent variable (spliced)
velocyto.estimation.clusters_stats(U: numpy.ndarray, S: numpy.ndarray, clusters_uid: numpy.ndarray, cluster_ix: numpy.ndarray, size_limit: int = 40) → typing.Tuple[numpy.ndarray, numpy.ndarray][source]

Calculate the averages per cluster

If the cluster is too small (size<size_limit) the average of the toal is reported instead