Nebula

v4.64

Analysis Status

📊 Data matrix

Try example data

Copy & Paste

Clean Data Matrix File

DIANN Protein Group Matrix

Row ID column for plots:

Expected format: DIANN `results.pg*_matrix.tsv`

DIANN Gene Group Matrix

Expected format: DIANN `results.gg*_matrix.tsv`

Data Format: TSV or CSV

First row: Column headers

First column: Row IDs

DIANN mode: Nebular has built-in parser for `results.pg*_matrix.tsv` and `results.gg*_matrix.tsv`

📋 Meta table

Upload Meta Table

Copy & Paste

Browse & Load File

Note: Meta table is auto-generated from matrix column names when you load a data matrix. Upload here to override.

Editable: Use Data PreProcess → Meta Table sub-tab to edit cells (Sample_ID is read-only).

💾 Session snapshot (JSON / report JS)

Save or reload the matrix, meta table, heatmap clustering result, PCA/t-SNE (if computed), optionally the Clustergrammer network, plus Differential, Enrichr, and SAINT results when present (formatVersion 3+). The JSON includes a plotting section with heatmap / PCA / t-SNE group-annotation checkbox and color-scheme choices. Export as qc_session.js and place it at data/qc_session.js to auto-load as a bundled report (see README).

Include Clustergrammer network (larger file)

Tip: Large matrices and the Clustergrammer network increase file size; uncheck the include option if you only need matrix, meta, and plotting settings.

Row Filter

Filter Rows by Valid values

Scope

Minimum count of valid values Minimum percentage of valid values

Threshold

Column Filter

Column Select

Check or uncheck sample columns here. Pattern select, Choose by groups, and Valid values can update the same checkboxes.

Sel	Column

Pattern select

Pattern (substring / regex)

Contains Regex

Choose by groups

Group by (meta column) Then by (optional)

Choose a grouping column to list groups.

Filter columns by Valid values

Scope

Minimum count of valid values Minimum percentage of valid values

Threshold

DIANN filters

Filter by DIANN pg-matrix annotation columns, then Apply to matrix. Only rows that pass all enabled criteria are kept for downstream analysis.

Name mapping

Map matrix row IDs to gene symbols, protein names, and UniProt accessions via MyGene.info (free). Results are stored for feature labels and Row Profile search (like DIANN annotations).

Species NCBI taxid (if Other) Interpret row IDs as

Idle.

API: MyGene.info (no key). Large tables are queried in batches; use Stop to keep partial results.

Offline: Serve over http://localhost if the browser blocks requests.

Annotation table: row order, sort, and pagination follow the Data Filter sub-tab.

Log

Meta Table

Note: Meta table is auto-generated from matrix column names. Override by uploading in Data Preparation.

Editable: Click cells to edit (Sample_ID is read-only)

Batch edit: Ctrl/Cmd+click to multi-select cells in one column, Shift+click for range, or use "Select All Visible (Same Column)".

Overall

High-level view of the loaded intensity matrix (current Data Filter result when applicable). Quantified ID = finite value > 0 in a cell.

Per-column summary (Plotly)

Bar metric

Uses all columns. Transform / treat-zero follow the Column correlation sidebar (shared controls).

Load a data matrix in Data Preparation (or apply filters), then use Refresh dashboard.

Row Profile Bar Chart

Row label (list & plot legend)

Uses matrix row IDs if DIANN protein-group annotations are not loaded.

Load data to search and select IDs.

Selected: 0 rows

Plot Settings

Plot type Color theme Use log scale (Y axis) Z-score per row (across samples) — normalize each selected row to mean 0, sd 1 so trends are comparable between rows

Tip: Search and select rows above, then click "Plot selected rows".

Row profile plot not generated yet

Use quick selection or the checklist in the sidebar, then click "Plot selected rows".

Column profile

Per sample column: ranked intensities, cumulative %, treemap, pie, and log10(1+I) histogram + density for all features (interactive Plotly). Pick a sample in the sub-tabs above the plots.

Display options

Row/feature label Max features (bar, curve, treemap, pie) Use log10(1 + intensity) for bar & line chart

Column Correlation

Sample–sample QC: use Overall correlations for subset heatmaps, Paired correlation for scatter / QQ / Bland–Altman. Transform and zero-handling also apply to Data QC → Overall per-column summary.

Shared scale

Transform Treat 0 as missing

Pair of columns

Column X Column Y Feature label (hover) Bland–Altman

Matrices (subset)

Correlation metric Max columns in heatmaps Distance from correlation

Heatmap

Clustering

Cluster features (rows) Cluster samples (columns) Distance metric Linkage

Colors

Color scale Reverse scale (high = cold, low = hot)

Row labels

Feature label

Uses DIANN columns and/or Name mapping (Data PreProcess) when loaded. Changing this redraws the plot without re-clustering.

Figure & margins

Title (optional)

Width (px)

Height (px)

Row dendro %

Column dendro %

Margin left (px)

Margin right (px)

Margin bottom (px)

Increase if angled column names are clipped; 0 is allowed. The framed area scrolls if the figure is larger than the panel.

Heatmap not generated yet

Click "Generate Heatmap" button to create the visualization

Clustergrammer

Feature label (rows)

Applies to Cluster and visualize (current matrix) and to the heatmap-side Clustergrammer when a heatmap exists. Changing it refreshes an open Clustergrammer tab or rebuilds from the cached heatmap.

Distance Linkage Pre-filter rows by variance (optional) Log10 transform Z-score per row

Use current data to start.

PCA Controls

PC to plot on X-axis: PC to plot on Y-axis: PC to plot on Z-axis (3D): Show Column Labels Auto label font/color Label font size (manual): Label color (manual): Auto show arrows Show arrows (manual) Show group confidence ellipses (95%) Plot Width (px): Plot Height (px): Main title (optional): Max Samples for PCA: Max Features for PCA:

Reducing features significantly speeds up PCA for large datasets

Number of Components: Fast Mode (fewer iterations, faster but less precise)

Performance: Using optimized typed arrays for faster computation. For best speed, reduce features to 200-500.

Data Preprocessing: Log10 Transform Column-wise Z-score Normalization Row-wise Z-score Normalization

PCA plot not generated yet

Click "Generate PCA Plot" button to create the visualization

PCA 3D plot not generated yet

Generate PCA to explore interactive 3D rotation, zoom, and metadata tooltips.

t-SNE Controls

Perplexity (5–50): Max iterations: Show Column Labels Auto label font/color Label font size (manual): Label color (manual): Auto show arrows Show arrows (manual) Plot Width (px): Plot Height (px): Main title (optional): Max Samples: Max Features:

t-SNE works well for many samples; limit features for speed.

Data Preprocessing: Log10 Transform Column-wise Z-score Normalization Row-wise Z-score Normalization

t-SNE plot not generated yet

Click "Generate t-SNE Plot" to create the embedding and scatter plot

UpSet / Venn / Karnaugh

Each set is one sample column. A row (feature) belongs to a set if that cell passes the presence rule below. Three linked views (see UpSet.js components) use UpSet.js (AGPL-3.0). Each chart’s Pop out button (next to its title in the main panel) opens popout/upset_popout.html via postMessage (needed for file://); allow pop-ups. The Heatmap tab uses the same pattern with popout/heatmap_popout.html. Plot toolbar PNG/SVG/dump/VEGA stay on the default UpSet.js controls.

Presence rule

Present if value is a finite non-zero number (0 counts as missing)

Present if intensity is strictly greater than a threshold (low values = low confidence; 0 still counts as missing):

Threshold Default 0 ⇒ same as >0

Set combinations (UpSet plot)

Same idea as UpSet.js App — applies only to the UpSet bar view (see data / generateCombinations).

Ordering

Mode

Minimum set members (degree)

Maximum set members (degree)

Max # combinations (after sort)

Include empty combinations

Hover a region in any view to highlight the same intersection in the others (linked selection).

UpSet

Venn diagram

Karnaugh map

Comparison

Comparison mode Group by (meta column)

Group A (Usually "Baseline" group) Group B (Usually "Treatment" group)

Significance cutoffs

|log2FC|

Fold change

p-value cutoff

FDR cutoff (when BH)

Data & test

Feature label (table & hover)

Log2(x+1) 0 / invalid = missing

Min valid / group

FC pseudocount

Test (two groups) Multiple testing

Plots & display

Volcano Y-axis Show labels for significantly changed

Top N features by raw p-value; matrix shows group means on the tested scale (optional row z-score). Run multi-group analysis first.

Showing only Significant Changed

Feature	mean A	mean B	log2FC	t	df	p	FDR	sig

SAINT

Uses the Data Preparation matrix and meta table. Map a meta column to control vs treatment, and choose a Bait column.

Sample grouping

Use Status column as-is (values T / C) Group column (when not using Status as-is) Control value → C Treatment value → T Bait column (required)

Input settings

Input level Protein column name Peptide column name Fragment column name

No results yet. Run SAINT after loading matrix + meta.

Enrichr

Open this tab to load libraries.

Plot options

Uses the full Enrichr term list (not only the current results page). Pick a view below; use Redraw plots after changing options.

Results heatmap

Same clustering pipeline and layout as the top-level Heatmap tab, but the plotted matrix comes from the Enrichr Results table: rows = ontology / library terms, columns = per-sample intensities attached to those terms (not the original protein × sample matrix). Statistics below only filter which terms are included.

Row filters

Leave a field empty to skip that filter.

Max adj. P-value Max P-value Min combined score Min odds ratio Min overlap (genes)

Clustering

Cluster ontology terms (rows) Cluster samples (columns) Distance metric Linkage

Colors

Color scale Reverse scale

Figure & dendrogram %

Title (optional)

Width px

Height px

Row dendro % Column dendro % Margin L Margin R Margin B

From the Results sub-tab, run enrichment with a loaded matrix so each term has per-sample intensities. Adjust filters, then Generate clustered heatmap (ontology terms × samples). Group bars use the same meta checkboxes as the Heatmap tab when columns match Sample_ID.

📊 Data matrix

Try example data

Copy & Paste

Clean Data Matrix File

DIANN Protein Group Matrix

DIANN Gene Group Matrix

📋 Meta table

📋 Example Meta Data

Upload Meta Table

Copy & Paste

Browse & Load File

💾 Session snapshot (JSON / report JS)

Meta Table

Export meta table

Batch Edit Selected Cells

Per-column summary

Distribution across features (box plot)

Total log signal per sample

Row Profile Bar Chart

Quick selection

Plot Settings

Column profile

Display options

Ranked intensity (bars + connecting line)

Cumulative % of total (sorted order)

Treemap (top N + Other)

Pie chart (top N + Other)

Intensity distribution (all features, log10 scale)

Column Correlation

Shared scale

Pair of columns

Matrices (subset)

Scatter-correlation matrix (lower scatter / upper corr)

Distance matrix (subset)

Pair scatter (Y vs X)

QQ plot (sorted quantiles)

Bland–Altman

Correlation with column X

Heatmap

Clustering

Colors

Sampling

Preprocessing

Row labels

Sample group overlays

Clustergrammer

PCA Controls

Group Annotation Labels

t-SNE Controls

Group Annotation Labels

UpSet / Venn / Karnaugh

Set chooser

Presence rule

UpSet

Venn diagram

Karnaugh map

Differential analysis

Comparison

Significance cutoffs

Data & test

Plots & display

SAINT

Sample grouping

Input settings

Analysis settings

Analysis log

Network

Filter thresholds

Scatter plot

Enrichr

Gene list source

Plot options

Results heatmap

Row filters

Clustering

Sampling (after filters)

Preprocessing

Colors

Document

Outline