cellrank.kernels.CytoTRACEKernel#

class cellrank.kernels.CytoTRACEKernel(adata, backward=False, **kwargs)[source]#

Kernel which computes directed transition probabilities based on a KNN graph and the CytoTRACE score [Gulati et al., 2020].

The KNN graph contains information about the (undirected) connectivities among cells, reflecting their similarity. CytoTRACE can be used to estimate cellular plasticity and in turn, a pseudotemporal ordering of cells from more plastic to less plastic states. It relies on the assumption that differentiated cells express, on average, less genes than naive cells. This kernel internally uses the cellrank.kernels.PseudotimeKernel to direct the kNN graph on the basis of the CytoTRACE-derived pseudotime.

Optionally, we apply a density correction as described in [Coifman et al., 2005], where we use the implementation of [Haghverdi et al., 2016].

Parameters:

Example

Workflow:

# import packages and load data
import scvelo as scv
import cellrank as cr
adata = cr.datasets.pancreas()

# standard pre-processing
sc.pp.filter_genes(adata, min_cells=10)
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)

# CytoTRACE by default uses imputed data - a simple way to compute kNN-imputed data is to use scVelo's moments
# function. However, note that this function expects `spliced` counts because it's designed for RNA velocity,
# so we're using a simple hack here:
if 'spliced' not in adata.layers or 'unspliced' not in adata.layers:
    adata.layers['spliced'] = adata.X
    adata.layers['unspliced'] = adata.X

# compute kNN-imputation using scVelo's moments function
scv.pp.moments(adata)

# import and initialize the CytoTRACE kernel, compute transition matrix - done!
from cellrank.kernels import CytoTRACEKernel
ctk = CytoTRACEKernel(adata).compute_cytotrace().compute_transition_matrix()

Attributes table#

adata

Annotated data object.

backward

Direction of the process.

connectivities

Underlying connectivity matrix.

kernels

Underlying base kernels.

params

Parameters which are used to compute the transition matrix.

pseudotime

Pseudotemporal ordering of cells.

shape

(n_cells, n_cells).

transition_matrix

Row-normalized transition matrix.

Methods table#

compute_cytotrace([layer, aggregation, ...])

Re-implementation of the CytoTRACE algorithm [Gulati et al., 2020] to estimate cellular plasticity.

compute_transition_matrix([...])

Compute transition matrix based on kNN graph and pseudotemporal ordering.

copy(*[, deep])

Return a copy of self.

from_adata(adata, key[, copy])

Read kernel object saved using write_to_adata().

plot_projection([basis, key_added, ...])

Plot transition_matrix as a stream or a grid plot.

plot_random_walks([n_sims, max_iter, seed, ...])

Plot random walks in an embedding.

plot_single_flow(cluster, cluster_key, time_key)

Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021].

read(fname[, adata, copy])

De-serialize self from a file.

write(fname[, write_adata, ext])

Serialize self to a file.

write_to_adata([key, copy])

Write the transition matrix and parameters used for computation to the underlying adata object.

Attributes#

adata#

CytoTRACEKernel.adata#

Annotated data object.

Return type:

AnnData

backward#

CytoTRACEKernel.backward#

Direction of the process.

Return type:

bool

connectivities#

CytoTRACEKernel.connectivities#

Underlying connectivity matrix.

Return type:

csr_matrix

kernels#

CytoTRACEKernel.kernels#

Underlying base kernels.

Return type:

Tuple[KernelExpression, ...]

params#

CytoTRACEKernel.params#

Parameters which are used to compute the transition matrix.

Return type:

Dict[str, Any]

pseudotime#

CytoTRACEKernel.pseudotime#

Pseudotemporal ordering of cells.

Return type:

Optional[array]

shape#

CytoTRACEKernel.shape#

(n_cells, n_cells).

Return type:

Tuple[int, int]

transition_matrix#

CytoTRACEKernel.transition_matrix#

Row-normalized transition matrix.

Return type:

Union[ndarray, csr_matrix]

Methods#

compute_cytotrace#

CytoTRACEKernel.compute_cytotrace(layer='Ms', aggregation=CytoTRACEAggregation.MEAN, use_raw=False, n_genes=200)[source]#

Re-implementation of the CytoTRACE algorithm [Gulati et al., 2020] to estimate cellular plasticity.

Computes the number of genes expressed per cell and ranks genes according to their correlation with this measure. Next, it selects to top-correlating genes and aggregates their (imputed) expression to obtain the CytoTRACE score. A high score stands for high differentiation potential (naive, plastic cells) and a low score stands for low differentiation potential (mature, differentiation cells).

Parameters:
  • layer (Optional[str]) – Key in anndata.AnnData.layers or ‘X’ for anndata.AnnData.X from where to get the expression.

  • aggregation (Literal[‘mean’, ‘median’, ‘hmean’, ‘gmean’]) –

    How to aggregate expression of the top-correlating genes. Valid options are:

    • ’mean’ - arithmetic mean.

    • ’median’ - median.

    • ’hmean’ - harmonic mean.

    • ’gmean’ - geometric mean.

  • use_raw (bool) – Whether to use the anndata.AnnData.raw to compute the number of genes expressed per cell (#genes/cell) and the correlation of gene expression across cells with #genes/cell.

  • n_genes (int) – Number of top positively correlated genes to compute the CytoTRACE score.

Return type:

CytoTRACEKernel

Returns:

Nothing, just modifies anndata.AnnData.obs with the following keys:

  • ’ct_score’ - the normalized CytoTRACE score.

  • ’ct_pseudotime’ - associated pseudotime, essentially 1 - CytoTRACE score.

  • ’ct_num_exp_genes’ - the number of genes expressed per cell, basis of the CytoTRACE score.

It also modifies anndata.AnnData.var with the following keys:

  • ’ct_gene_corr’ - the correlation as specified above.

  • ’ct_correlates’ - indication of the genes used to compute the CytoTRACE score, i.e. the ones that correlated positively with ‘ct_num_exp_genes’.

Notes

This will not exactly reproduce the results of the original CytoTRACE algorithm [Gulati et al., 2020] because we allow for any normalization and imputation techniques whereas CytoTRACE has built-in specific methods for that.

compute_transition_matrix#

CytoTRACEKernel.compute_transition_matrix(threshold_scheme='hard', frac_to_keep=0.3, b=10.0, nu=0.5, check_irreducibility=False, n_jobs=None, backend='loky', show_progress_bar=True, **kwargs)#

Compute transition matrix based on kNN graph and pseudotemporal ordering.

Depending on the choice of the threshold_scheme, it is based on ideas by either Palantir [Setty et al., 2019] or VIA [Stassen et al., 2021].

Parameters:
  • threshold_scheme (Union[Literal[‘soft’, ‘hard’], Callable[[float, ndarray, ndarray], ndarray]]) –

    Which method to use when biasing the graph. Valid options are:

    • ’hard’ - based on Palantir [Setty et al., 2019] which removes some edges that point against the direction of increasing pseudotime. To avoid disconnecting the graph, it does not remove all edges that point against the direction of increasing pseudotime, but keeps the ones that point to cells inside a close radius. This radius is chosen according to the local cell density.

    • ’soft’ - based on VIA [Stassen et al., 2021] which down-weights edges that points against the direction of increasing pseudotime. Essentially, the further “behind” a query cell is in pseudotime with respect to the current reference cell, the more penalized will be its graph-connectivity.

    • callable - any function conforming to the signature of cellrank.kernels.utils.ThresholdSchemeABC.__call__().

  • frac_to_keep (float) – Fraction of the closest neighbors (according to graph connectivities) are kept, no matter whether they lie in the pseudotemporal past or future. This is done to ensure that the graph remains connected. Only used when threshold_scheme = 'hard'. Needs to fall within the interval [0, 1].

  • b (float) – The growth rate of generalized logistic function. Only used when threshold_scheme = 'soft'.

  • nu (float) – Affects near which asymptote maximum growth occurs. Only used when threshold_scheme = 'soft'.

  • check_irreducibility (bool) – Optional check for irreducibility of the final transition matrix.

  • show_progress_bar (bool) – Whether to show a progress bar. Disabling it may slightly improve performance.

  • n_jobs (Optional[int]) – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.

  • backend (Literal[‘loky’, ‘multiprocessing’, ‘threading’]) – Which backend to use for parallelization. See joblib.Parallel for valid options.

  • kwargs (Any) – Keyword arguments for threshold_scheme.

Return type:

PseudotimeKernel

Returns:

Self and updates transition_matrix and params.

copy#

CytoTRACEKernel.copy(*, deep=False)#

Return a copy of self.

Return type:

Kernel

from_adata#

classmethod CytoTRACEKernel.from_adata(adata, key, copy=False)#

Read kernel object saved using write_to_adata().

Parameters:
Return type:

Kernel

Returns:

The kernel with explicitly initialized properties:

plot_projection#

CytoTRACEKernel.plot_projection(basis='umap', key_added=None, recompute=False, stream=True, connectivities=None, **kwargs)#

Plot transition_matrix as a stream or a grid plot.

Parameters:
Return type:

None

Returns:

Nothing, just plots and modifies anndata.AnnData.obsm with a key based on key_added.

plot_random_walks#

CytoTRACEKernel.plot_random_walks(n_sims=100, max_iter=0.25, seed=None, successive_hits=0, start_ixs=None, stop_ixs=None, basis='umap', cmap='gnuplot', linewidth=1.0, linealpha=0.3, ixs_legend_loc=None, n_jobs=None, backend='loky', show_progress_bar=True, figsize=None, dpi=None, save=None, **kwargs)#

Plot random walks in an embedding.

This method simulates random walks on the Markov chain defined though the corresponding transition matrix. The method is intended to give qualitative rather than quantitative insights into the transition matrix. Random walks are simulated by iteratively choosing the next cell based on the current cell’s transition probabilities.

Parameters:
  • n_sims (int) – Number of random walks to simulate.

  • max_iter (Union[int, float]) – Maximum number of steps of a random walk. If a float, it can be specified as a fraction of the number of cells.

  • seed (Optional[int]) – Random seed.

  • successive_hits (int) – Number of successive hits in the stop_ixs required to stop prematurely.

  • start_ixs (Union[Sequence[str], Mapping[str, Union[str, Sequence[str], Tuple[float, float]]], None]) –

    Cells from which to sample the starting points. If None, use all cells. Can be specified as:

    For example {'dpt_pseudotime': [0, 0.1]} means that starting points for random walks will be sampled uniformly from cells whose pseudotime is in [0, 0.1].

  • stop_ixs (Union[Sequence[str], Mapping[str, Union[str, Sequence[str], Tuple[float, float]]], None]) –

    Cells which when hit, the random walk is terminated. If None, terminate after max_iters. Can be specified as:

    For example {'clusters': ['Alpha', 'Beta']} and successive_hits = 3 means that the random walk will stop prematurely after cells in the above specified clusters have been visited successively 3 times in a row.

  • basis (str) – Basis in anndata.AnnData.obsm to use as an embedding.

  • cmap (Union[str, LinearSegmentedColormap]) – Colormap for the random walk lines.

  • linewidth (float) – Width of the random walk lines.

  • linealpha (float) – Alpha value of the random walk lines.

  • ixs_legend_loc (Optional[str]) – Legend location for the start/top indices.

  • show_progress_bar (bool) – Whether to show a progress bar. Disabling it may slightly improve performance.

  • n_jobs (Optional[int]) – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.

  • backend (str) – Which backend to use for parallelization. See joblib.Parallel for valid options.

  • figsize (Optional[Tuple[float, float]]) – Size of the figure.

  • dpi (Optional[int]) – Dots per inch.

  • save (Union[str, Path, None]) – Filename where to save the plot.

  • kwargs (Any) – Keyword arguments for scvelo.pl.scatter().

Return type:

None

Returns:

Nothing, just plots the figure. Optionally saves it based on save. For each random walk, the first/last cell is marked by the start/end colors of cmap.

plot_single_flow#

CytoTRACEKernel.plot_single_flow(cluster, cluster_key, time_key, clusters=None, time_points=None, min_flow=0, remove_empty_clusters=True, ascending=False, legend_loc='upper right out', alpha=0.8, xticks_step_size=1, figsize=None, dpi=None, save=None, show=True)#

Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021].

Parameters:
  • cluster (str) – Cluster for which to visualize outgoing flow.

  • cluster_key (str) – Key in anndata.AnnData.obs where clustering is stored.

  • time_key (str) – Key in anndata.AnnData.obs where experimental time is stored.

  • clusters (Optional[Sequence[Any]]) – Visualize flow only for these clusters. If None, use all clusters.

  • time_points (Optional[Sequence[Union[float, int]]]) – Visualize flow only for these time points. If None, use all time points.

  • min_flow (float) – Only show flow edges with flow greater than this value. Flow values are always in [0, 1].

  • remove_empty_clusters (bool) – Whether to remove clusters with no incoming flow edges.

  • ascending (Optional[bool]) – Whether to sort the cluster by ascending or descending incoming flow. If None, use the order as in defined by clusters.

  • alpha (Optional[float]) – Alpha value for cell proportions.

  • xticks_step_size (Optional[int]) – Show only every other n-th tick on the x-axis. If None, don’t show any ticks.

  • legend_loc (Optional[str]) – Position of the legend. If None, do not show the legend.

  • figsize (Optional[Tuple[float, float]]) – Size of the figure.

  • dpi (Optional[int]) – Dots per inch.

  • save (Union[str, Path, None]) – Filename where to save the plot.

  • show (bool) – If False, return matplotlib.pyplot.Axes.

Return type:

Optional[Axes]

Returns:

The axes object, if show = False. Nothing, just plots the figure. Optionally saves it based on save.

Notes

This function is a Python re-implementation of the following original R function with some minor stylistic differences. This function will not recreate the results from [Mittnenzweig et al., 2021], because there, the Metacell model [Baran et al., 2019] was used to compute the flow, whereas here the transition matrix is used.

read#

static CytoTRACEKernel.read(fname, adata=None, copy=False)#

De-serialize self from a file.

Parameters:
  • fname (Union[str, Path]) – Filename from which to read the object.

  • adata (Optional[AnnData]) – anndata.AnnData object to assign to the saved object. Only used when the saved object has adata and it was saved without it.

  • copy (bool) – Whether to copy adata before assigning it or not. If adata is a view, it is always copied.

Return type:

IOMixin

Returns:

The de-serialized object.

write#

CytoTRACEKernel.write(fname, write_adata=True, ext='pickle')#

Serialize self to a file.

Parameters:
  • fname (Union[str, Path]) – Filename where to save the object.

  • write_adata (bool) – Whether to save adata object or not, if present.

  • ext (Optional[str]) – Filename extension to use. If None, don’t append any extension.

Return type:

None

Returns:

Nothing, just writes itself to a file using pickle.

write_to_adata#

CytoTRACEKernel.write_to_adata(key=None, copy=False)#

Write the transition matrix and parameters used for computation to the underlying adata object.

Parameters:

key (Optional[str]) – Key used when writing transition matrix to adata. If None, the key will be determined automatically.

Return type:

None

Returns:

Updates the adata with the following fields:

  • .obsp['{key}'] - the transition matrix.

  • .uns['{key}_params'] - parameters used for the calculation.