Understanding EasyDeconResult

EasyDeconResult is returned by:

result = ed.run_easydecon(..., return_result_object=True)

Field	Shape/type	Meaning	Typical use
`markers_df`	marker rows x metadata columns	Spatial-compatible markers after role routing and final de-duplication	Marker QC
`phase1_result`	locations x groups	Thresholded Phase 1 expression evidence	Presence detection
`priors_df`	locations x groups	Row-normalized Phase 1 evidence	Prior gating and refinement
`phase2_result`	locations x groups	Raw Phase 2 similarity or rank evidence	Method diagnostics
`likelihoods_df`	locations x groups	Normalized Phase 2 evidence	Compare Phase 2 support
`posterior_df`	locations x groups or `None`	Combined prior and likelihood support	Preferred probabilistic matrix
`assignment_df`	locations x groups	Matrix used for hard assignment	Reassignment and inspection
`assigned_labels`	locations x assignment columns	Final categorical labels	Plotting and annotation
`diagnostics`	`dict`	Marker, Phase 2, assignment, and workflow metadata	QC and reproducibility
`prepared_markers`	`PreparedMarkers` or `None`	Reusable marker preparation passed into the workflow	Marker reuse

Which matrix should I use?

Spatial support plots: posterior_df when available.
Phase 1 presence maps: priors_df.
Phase 2 method debugging: phase2_result.
Hard cell-type maps: assigned_labels.
Reassignment without rescoring: assignment_df.
List-style marker mask workflow: assignment_df or phase2_result.

List-style marker_genes exception

When marker_genes is a plain list, Phase 1 is used as a row mask rather than a cell-type-specific prior matrix. In that workflow posterior_df is None and assignment_df is phase2_result.

Interpretation warnings

Posterior rows are relative support among tested marker groups. They are not automatically absolute biological cell fractions. Spatial locations may contain mixtures, and hard labels discard uncertainty.

Assignment counts are counts of spatial units, not biological cell counts. Depending on the data, a unit may be a spot, bin, or segmented cell.

Candidate-pruned matrices

With phase2_candidate_pruning=True, noncandidate group entries are zero in phase2_result and likelihoods_df. With phase2_candidate_threshold=0.0 and prior_weight > 0, the posterior is normally preserved for non-negative methods, but positive thresholds can change posterior_df and assigned_labels.

Candidate pruning diagnostics live under:

result.diagnostics["phase2"]["performance"]

Marker-role diagnostics

Role routing diagnostics live under:

result.diagnostics["marker_roles"]

They include the routing mode, roles used in Phase 1 and Phase 2, marker counts by group, and marker counts by role when available.

Summary helpers

summary = ed.summarize_easydecon_result(
    result,
    sdata=sdata,
    as_dataframe=False,
)

marker_summary = ed.summarize_marker_table(result.markers_df)

summarize_easydecon_result reports marker counts, workflow settings, matrix shapes, row-sum statistics, assignment counts, and optional spatial alignment checks. summarize_marker_table returns compact per-group marker counts and top genes.