FastCCC Reference-Based Inference Report Reference Analysis

Query: {{ query_name }}  vs  Reference: {{ reference_name }}
{{ report_date }}  |  DB: {{ database_name }}  |  FastCCC v{{ version }}

1. Executive Summary

{{ '{:,}'.format(n_tested) }}
Total interactions tested
{{ '{:,}'.format(n_significant) }}
Significant in {{ query_name }}
{{ '{:,}'.format(n_in_reference) }}
Matched in reference
{{ '{:,}'.format(n_lr_pairs) }}
Unique L-R pairs
{{ '{:,}'.format(n_ct_pairs) }}
Active cell-type pairs
{% set total_ref = trend_counts['Up'] + trend_counts['Both Sig'] + trend_counts['Down'] + trend_counts['Both NS'] %} {% if total_ref > 0 %}
{% for t in trend_order %} {% set pct = (trend_counts[t] / total_ref * 100) | round(1) %}
{% if pct > 6 %}{{ t }} {{ pct }}%{% endif %}
{% endfor %}
{% for t in trend_order %}
{{ t }}: {{ '{:,}'.format(trend_counts[t]) }}
{% endfor %}
{% endif %}

FastCCC compared {{ query_name }} against the selected {{ reference_name }} reference panel, testing {{ '{:,}'.format(n_tested) }} L-R interactions across {{ '{:,}'.format(n_ct_pairs) }} cell-type pairs. Of the {{ '{:,}'.format(n_in_reference) }} interactions matched to the reference: {{ '{:,}'.format(trend_counts['Up']) }} were {{ query_name }}-specific (Up), {{ '{:,}'.format(trend_counts['Both Sig']) }} were shared (Both Sig), {{ '{:,}'.format(trend_counts['Down']) }} were {{ reference_name }}-specific (Down), and {{ '{:,}'.format(trend_counts['Both NS']) }} were background (Both NS).

Report Parameters

{% if reference_panel %} {% endif %}
Query condition{{ query_name }}
Reference panel{{ reference_name }}
Reference source{{ reference_panel.source_label }}
Reference panel ID{{ reference_panel.reference_name }}
Reference cell types in config{{ reference_panel.n_celltypes }}
Reference min percentile{{ reference_panel.min_percentile }}
LRI database{{ database_name }}
Report generated{{ report_date }}
FastCCC version{{ version }}
Interpretation scope. Reference inference assigns query significance and query-versus-reference trend labels after aligning eligible cell types and L-R interactions to the selected reference panel. Interactions without a matched reference context remain query results and should not be read as Up or Down reference trends.

2. Global CCC Trends

Fig A — Overall Trend Distribution

{% if figs.trend_dist[0] %} Fig A

{{ figs.trend_dist[1] }}

{% else %}

{{ figs.trend_dist[1] }}

{% endif %}

Fig B — Per Cell-Type Pair Trend Breakdown

{% if figs.ct_breakdown[0] %} Fig B

{{ figs.ct_breakdown[1] }}

{% else %}

{{ figs.ct_breakdown[1] }}

{% endif %}

3. Sender–Receiver Heatmaps

Fig C — {{ query_name }}-specific Interactions (Up)

{% if figs.heatmap_up[0] %} Fig C

{{ figs.heatmap_up[1] }}

{% else %}

{{ figs.heatmap_up[1] }}

{% endif %}

Fig D — {{ reference_name }}-specific Interactions (Down)

{% if figs.heatmap_down[0] %} Fig D

{{ figs.heatmap_down[1] }}

{% else %}

{{ figs.heatmap_down[1] }}

{% endif %}

4. Top L-R Pair Dotplots

Fig E — Top {{ query_name }}-specific L-R Pairs (Up)

{% if figs.dotplot_up[0] %} Fig E

{{ figs.dotplot_up[1] }}

{% else %}

{{ figs.dotplot_up[1] }}

{% endif %}

Fig F — Top {{ reference_name }}-specific L-R Pairs (Down)

{% if figs.dotplot_down[0] %} Fig F

{{ figs.dotplot_down[1] }}

{% else %}

{{ figs.dotplot_down[1] }}

{% endif %}

5. Communication Score Distribution

Fig G — Query CS by Reference Trend

{% if figs.cs_scatter[0] %} Fig G

{{ figs.cs_scatter[1] }}

{% else %}

{{ figs.cs_scatter[1] }}

{% endif %}

6. Pathway Breakdown

Fig H — Up vs Down Interactions by Pathway

{% if figs.pathway_breakdown[0] %} Fig H

{{ figs.pathway_breakdown[1] }}

{% else %}

{{ figs.pathway_breakdown[1] }}

{% endif %}

7. Cell-Type Reference Explorer

Fig I — Cell-Type Trend, L-R, and Pathway Profiles

{% if celltype_reference.profiles %}

Fig I. {{ celltype_reference.caption }}

{% else %}

{{ celltype_reference.caption }}

{% endif %}

8. Methods & Parameters

Reference panel construction. The selected {{ reference_name }} panel was prepared using build_reference_workflow, which applies rank-based preprocessing (rank_preprocess) and computes per-gene PMFs via FFT-based convolution across all cell types. L-R pair null distributions are derived analytically without permutation testing.

Query inference. The {{ query_name }} query dataset was digitised into the same rank space as the reference using infer_query_workflow. Communication scores (CS) were computed for each L-R × cell-type pair and compared against the reference null distribution (adjusted by a housekeeping-gene scaling factor) to assign significance and trend labels.

Trend categories. Up: significant in {{ query_name }} but not {{ reference_name }} (query-specific enrichment). Down: significant in {{ reference_name }} but not {{ query_name }} (reference-specific, lost in query). Both Sig: significant in both (shared reference-supported interaction). Both NS: not significant in either (background).

Visualisation. All figures were generated with Matplotlib/Seaborn at {{ version }}. Heatmaps show the top cell types by total interaction involvement. Dotplots rank L-R pairs by total communication score. The CS distribution panel uses log1p(CS) violin and box plots to compare communication-score ranges across trend categories.

9. Figure Generation Audit

Figure panels are generated independently so an unsupported or empty panel does not mask the remaining reference report.

{{ figure_audit.counts.generated }}Figures generated
{{ figure_audit.counts.skipped }}Panels skipped
{{ figure_audit.counts.failed }}Panels failed
{% if figure_audit.unavailable %} {% for item in figure_audit.unavailable %} {% endfor %}
ArtifactStatusReason
{{ item.artifact }}{{ item.status }}{{ item.note }}
{% else %}

All requested reference-report panels were generated.

{% endif %}