ehrapy.plot.ncp_cluster_trajectories

ehrapy.plot.ncp_cluster_trajectories#

ehrapy.plot.ncp_cluster_trajectories(edata, *, layer, cluster_key, key='ncp', n_top_diseases=5, sigmoid_transform=False, width=520, height=300)[source]#

Plot mean variable trajectories per cluster, guided by NCP loadings.

This function bridges unsupervised NCP decomposition and an existing cluster assignment (e.g. from sc.tl.leiden or a clinical grouping): for each cluster it identifies which NCP component best represents that cluster, selects the top variables of that component, and visualises their mean trajectories over the time axis — all from the raw data, not the low-rank approximation.

What each panel shows

One panel is drawn per unique value in edata.obs[cluster_key], arranged in two columns. The panel title shows the cluster label, the number of observations, and the dominant NCP component.

Within each panel, each line is one variable. The y-axis is the mean value (or mean probability, if sigmoid_transform=True) of that variable across all observations belonging to the cluster, plotted at each time point along the x-axis. Lines therefore reveal:

  • Level — which variables have the highest absolute values for this cluster (higher lines = more pronounced feature).

  • Shape — whether a variable rises, falls, peaks, or stays flat over time within the cluster.

  • Co-occurrence — variables that share a similar trajectory shape are likely driven by the same underlying mechanism.

How variables are chosen per cluster

  1. The mean patient loading A[mask].mean(axis=0) is computed for the cluster, giving a score per NCP component.

  2. The component with the highest score is called the dominant component.

  3. The n_top_diseases variables with the highest loading in that component’s variable factor B[:, dominant] are selected.

This means each cluster is represented by the clinical variables that the NCP model considers most characteristic of it, providing a direct link between the data-driven decomposition and the cluster structure.

Requires ncp() to have been run first.

Parameters:

  • edata (EHRData) – Central data object.

  • layer (str) – Key of the 3D layer holding the raw values (shape n_obs × n_vars × n_time). All values must be non-negative (use sigmoid_transform=True for logit layers, or np.abs / clipping beforehand).

  • cluster_key (str) – Column in edata.obs that contains cluster or group labels (any categorical or string column).

  • key (str, default: 'ncp') – Key under which NCP results are stored (matches key_added in ncp()).

  • n_top_diseases (int, default: 5) – Number of top-loaded variables to show per cluster.

  • sigmoid_transform (bool, default: False) – Apply a sigmoid transformation to the layer values before averaging. Set to True when the layer stores raw logits so that the y-axis represents probabilities in (0, 1).

  • width (int, default: 520) – Width of each panel in pixels.

  • height (int, default: 300) – Height of each panel in pixels.

Return type:

Layout

Returns:

HoloViews Layout with one panel per cluster, arranged in two columns.

Examples

>>> import ehrdata as ed, ehrapy as ep
>>> edata = ed.dt.ehrdata_blobs(n_variables=8, n_centers=3, n_observations=30, base_timepoints=12)
>>> ep.tl.ncp(edata, layer="tem_data", rank=3, sigmoid_transform=True)
>>> ep.pl.ncp_cluster_trajectories(edata, layer="tem_data", cluster_key="cluster")
../../_images/ncp_cluster_trajectories.png