Analysis mode: Detailed (variable + plots + statistical tests)#

Audience: everyone (best for computational users; still accessible for wet lab)
Time: 20–40 minutes
What you’ll learn:

  • How Detailed mode compares pages for a chosen variable

  • How to choose the right plot type for categorical vs continuous vs gene expression

  • What summary statistics and statistical tests are shown (and their limitations)

  • How to export plot data as CSV

Prerequisites:

  • A dataset loaded

  • At least one highlight page (Detailed compares pages)

  • For gene variables: gene expression available

What Detailed mode is for#

Detailed mode is the “one variable, many pages” workhorse.

You use it when you want to answer questions like:

  • “Does cell_type composition differ between these pages?”

  • “Is pct_mito higher in Page A than Page B?”

  • “Is gene CXCL8 more expressed in this page than the rest of the dataset?”

Compared to Quick:

  • Quick gives fast aggregated summaries.

  • Detailed gives plot choice + customization + side-by-side comparisons + tests.

Inputs (what you choose)#

Detailed mode has three core inputs:

1) Variable#

You choose one variable from:

  • Categorical obs (labels)

  • Continuous obs (numbers)

  • Gene expression (a gene)

2) Pages (“Compare pages”)#

You select which highlight pages are included in the comparison.

Detailed also supports derived pages:

  • Rest of <page> (the complement of a page across the whole dataset)

Common workflow:

  • select 1–4 pages for comparisons,

  • or do one-vs-rest by selecting Page A and Rest of Page A.

3) Plot type#

Plot types depend on variable kind:

  • Categorical variables: bar/pie/heatmap-style comparisons

  • Continuous variables (including genes): violin/box/histogram/density-style comparisons

Plot options live in the expanded/modal view (recommended for real work).

What you get (outputs)#

Detailed mode provides three layers of output:

A) The plot (main visualization)#

The plot visualizes the distribution of the selected variable across the selected pages.

Examples:

  • categorical → grouped/stacked bar plot

  • continuous → violin plot per page

  • gene expression → distribution plots per page

B) Summary statistics (table)#

The summary table is meant to be “readable truth” even when plots are ambiguous:

  • For categorical variables: per-page category counts/percentages (limited to the most common categories in the table view)

  • For continuous variables: per-page count/mean/median/min/max/std

C) Statistical annotations (tests)#

If you select at least 2 pages, Detailed shows statistical tests appropriate to the variable kind:

If the variable is categorical#

  • Chi-squared test for difference in distributions

  • effect size: Cramér’s V

If the variable is continuous (including genes)#

If you selected exactly 2 pages:

  • Welch’s t-test (effect size: Cohen’s d)

  • Mann–Whitney U (effect size: rank-biserial r)

If you selected 3+ pages:

  • One-way ANOVA (effect size: η²)

  • Kruskal–Wallis (effect size: ε²)

Important

These tests are meant for exploratory, interactive comparison.

They do not model batch covariates, do not correct for running many variables, and some p-values use normal-approximation formulas. For publication-grade inference, export data and use a dedicated statistical workflow.

Fast path (wet lab / non-technical)#

Goal: “Show me whether these two groups differ in a way I can explain.”

  1. Make two pages

    • Example: Responder and Non-responder (or Cluster 3 and Rest of Cluster 3).

  2. Open Analysis → Detailed

  3. Choose a variable

    • Start with a clear variable like cell_type or pct_mito.

  4. Select the pages to compare (Compare pages)

  5. Read the results

    • Plot: “Do the shapes look different?”

    • Summary stats: “Are the medians/means different?”

    • Statistical tests: “Is it likely a real difference?”

If you need gene-level answers, switch the variable type to Gene expression and choose a gene.

Practical path (computational users)#

Choosing the right plot type#

  • Bar plot: best default for categorical; use “Percentages” for composition comparisons across different page sizes.

  • Violin/Box plot: best for continuous distributions; violin shows shape, box shows summary.

  • Histogram/Density: useful for multi-modal distributions and QC gating.

Reading statistical tests responsibly#

Treat the tests as:

  • a check for “is there a detectable difference given this subset?”

  • not a substitute for a designed experiment model.

If you see significance with tiny effect sizes:

  • consider whether sample size is huge (small differences become “significant”),

  • and inspect effect size + plot shape, not just p-values.

Gene expression scale matters#

Detailed mode uses whatever expression values are in your dataset:

  • raw counts, log1p, normalized, etc.

This affects:

  • the magnitude of mean/median differences,

  • and how you should interpret “fold-like” effects (especially if values are already log-transformed).

Export (CSV)#

Detailed mode can export plot data as CSV.

Important: exports are plot-type-specific. For example:

  • distribution plots often export summary statistics per page (not per-cell values),

  • categorical plots export category counts/percentages,

  • some plots export binned counts (histograms).

If you need a “raw table of all values per cell”, you may need to export via a different workflow (e.g., in Python) depending on your use case.

Edge cases and pitfalls#

  • No pages selected / no pages exist → create highlight pages first.

  • Tiny pages → tests are unstable and effect sizes can be misleading.

  • Constant values (no variance) → tests can return degenerate statistics.

  • Many categories → tables may truncate; use bar plot + export.

  • Missing gene expression → gene variables unavailable or error on load.

  • Overlapping pages → comparisons are not independent; interpret cautiously.

Troubleshooting (Detailed mode)#

Symptom: “No variable options / gene expression says unavailable”#

Likely causes:

  • dataset was loaded without gene expression,

  • or the export did not include var/gene expression.

Fix:

Symptom: “Plot is empty but pages have cells”#

Likely causes:

  • the selected variable is missing for those cells (all values are missing/non-finite),

  • or categorical field has no values in those pages.

Fix:

  • try a different variable,

  • verify the field exists and is populated,

  • verify pages actually contain cells.

Screenshot placeholder (you will replace later)#

Placeholder screenshot for Detailed mode success state.

Detailed mode compares a chosen variable across selected pages, with plots, summary stats, and statistical tests in the expanded view.#

Next steps#