velocyto.estimation module

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

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

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

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

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

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

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

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) → Tuple[numpy.ndarray, numpy.ndarray]

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, return_R2: bool = False, limit_gamma: bool = False, bounds: Tuple[float, float] = (0, 20)) → numpy.ndarray

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) → Any

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) → Tuple[numpy.ndarray, numpy.ndarray]

Calculate the averages per cluster

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