scRNA QC Overview
2 nodessingleCellQCTemplateCompute QC metrics and generate violin plots — without filtering. Useful for inspecting data quality before deciding on thresholds.
ChainLoad H5AD → Quality Control
Built aroundscrna/pbmc3k_raw.h5ad
Working in the app
Templates & Analysis Wizard.
33 ready-made workflows in the Templates dialog, plus a three-step wizard that builds a linear pipeline from a data type and a goal. Both drop a wired graph on the canvas with parameters already set — a starting point to edit, not a black box.
01
How to use a template
- 01
Toolbar → Templates → pick one
If the current tab already has nodes, the app asks Replace Current Workflow? — open a new workflow tab first if you want to keep your graph. - 02
Point the loader at your data
Templates load with the loader path empty. Click the first node and choose your file or folder. No sample data ships in the installer; the sample datasets page lists the public data each template was built around. - 03
Run, then edit
Nodes execute in dependency order. Afterwards open any node, change a parameter and re-run just the part that changed.
10 of 33 need a one-time setup first
Templates marked Needs a resource contain a node that depends on an external database, prior network, model or manifest. Oreumics never downloads one in the middle of a run, so on a fresh install that node stops with a message naming the resource while the rest of the workflow completes. Install it once in the Resource Center and the template runs clean from then on.
02
The Analysis Wizard
For a first analysis, the wizard is quicker than choosing among 33 templates. It asks three questions — Pick your data type, Pick what you want to do, Review & build — and Build Workflow places a linear, runnable chain on the canvas, wired in order, exactly as a template would. It then tells you the one thing left to do: click the Load node and choose your file. 14 goals across the 5 modalities:
Single-cell RNA-seq
Drop-seq, 10x, Smart-seq — anything with genes × cells.
Just inspect quality (no filtering)
Load a single-cell file, compute QC metrics, and view the violin plots — without filtering yet.
Standard clustering pipeline
Normalize → log → HVG → scale → PCA → neighbors → UMAP → clustering, ending in a cluster plot.
Clustering + DEG (marker genes)
Standard clustering plus Differential Expression and a Top DEG Gene List per cluster.
Trajectory / pseudotime
Clustering plus Diffusion Map → DPT pseudotime → PAGA topology and a trajectory plot.
Clustering + DEG + pathway enrichment
Find markers per cluster, then run Enrichr-based pathway enrichment on the top DEG genes.
Spatial transcriptomics
Visium, MERFISH, Xenium — single-cell data that also has spatial coordinates per cell.
Cluster + spatial overview
Standard clustering, then view the cluster labels in spatial coordinates.
Spatial neighborhood enrichment
Adds the spatial neighbor graph and cluster co-localization enrichment.
Spatially variable genes (Moran's I)
Find genes whose expression is spatially structured using spatial autocorrelation.
Bulk RNA-seq
Count matrices, featureCounts, kallisto / salmon abundance — samples × genes.
Differential expression (DESeq2)
Load counts, attach a sample sheet via Merge Cell Meta, QC, filter, then DESeq2 between two groups.
DESeq2 + GSEA pathway analysis
Adds Gene Set Enrichment / ORA on top of the DESeq2 results.
ATAC-seq (chromatin accessibility)
scATAC-seq peak matrices and Cell Ranger ATAC output.
LSI + clustering
TF-IDF + SVD (LSI) → neighbors → UMAP → clustering of cells by accessibility.
LSI + clustering + motif analysis
Adds differential accessibility per cluster and motif enrichment on the top differential peaks.
DNA methylation (Illumina array)
450K / EPIC / EPIC v2 IDAT files or a pre-processed beta matrix.
Differentially methylated probes (DMP)
Load IDAT, attach a sample sheet, QC, normalize, filter, then DMP between two groups.
DMP + DMR regions
Adds Differentially Methylated Regions on top of the DMP results.
03
Single-cell RNA-seq (20)
scRNA Standard Clustering
12 nodessingleCellStandardTemplateThe standard single-cell pipeline — quality control filtering, normalization, PCA + UMAP embedding, and Leiden clustering.
ChainLoad H5AD → Quality Control → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → UMAP → Clustering → Cluster Visualization → Embedding Plot
Built aroundscrna/pbmc3k_raw.h5ad
scRNA Full Pipeline with Visualization
16 nodessingleCellFullPipelineTemplateChainLoad H5AD → Quality Control → Normalize → Log1p → Highly Variable Genes → HVG Visualization → Scale → PCA → PCA Visualization → Neighbors → UMAP → Clustering → Cluster Visualization → Embedding Plot → DEG Analysis → Marker Visualization
Built aroundscrna/pbmc3k_raw.h5ad
scRNA Marker Discovery
11 nodesmarkerDiscoveryTemplateBuild clusters and identify marker genes with dotplot visualization
ChainLoad H5AD → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → Clustering → DEG Analysis → Marker Visualization
Built aroundscrna/pbmc3k_raw.h5ad
scRNA DEG + Gene List Export
14 nodesdegExportTemplateNeeds a resourceChainLoad H5AD → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → Clustering → DEG Analysis → Top DEG Gene List → Pathway Enrichment → Export CSV → Pathway Visualization
Built aroundscrna/pbmc3k_raw.h5ad
SetupPathway Enrichment — install once
scRNA Doublet Screening
5 nodesdoubletScreeningTemplateCompute QC metrics, detect potential doublets, and inspect score distribution
ChainLoad H5AD → Quality Control → Doublet Detection → Doublet Visualization
Built aroundscrna/pbmc3k_raw.h5ad
scRNA Harmony Integration
14 nodesharmonyIntegrationTemplateMerge two datasets, run Harmony batch correction, and visualize integrated UMAP
ChainLoad H5AD → Merge Data → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Harmony → Neighbors → UMAP → Embedding Plot → Clustering
Built aroundscrna/pbmc3k_raw.h5ad
Trajectory with PAGA+DPT
14 nodestrajectoryTemplateBuild a trajectory graph and pseudotime ordering on the PBMC sample dataset (PAGA + DPT).
ChainLoad H5AD → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → UMAP → Clustering → PAGA → Diffusion Map → DPT → Draw Graph → Trajectory Visualization
Built aroundscrna/pbmc3k_raw.h5ad
RNA Velocity Stream (H5AD)
4 nodesvelocityStreamTemplateRun stochastic RNA velocity and visualize stream on UMAP from pre-built H5AD
ChainLoad H5AD → RNA Velocity → Velocity Graph → Velocity Visualization
Built aroundvelocity/dentategyrus_velocity.h5ad
RNA Velocity Latent Time
6 nodesvelocityLatentTimeTemplateRun dynamical velocity, infer latent time and terminal states
ChainLoad H5AD → RNA Velocity → Velocity Graph → Latent Time → Terminal States → Velocity Visualization
Built aroundvelocity/dentategyrus_velocity.h5ad
RNA Velocity from Velocyto Loom
4 nodesvelocytoLoomTemplateLoad velocyto .loom output directly, run stochastic velocity, and visualize stream
ChainLoad Velocyto Loom → RNA Velocity → Velocity Graph → Velocity Visualization
Built aroundvelocity/velocyto_demo.loom
RNA Velocity from STARsolo
4 nodesstarVelocityTemplateLoad STARsolo Velocyto output, run stochastic velocity, and visualize arrow plot
ChainLoad STARsolo Velocity → RNA Velocity → Velocity Graph → Velocity Visualization
Pathway Enrichment Analysis
11 nodespathwayAnalysisTemplateNeeds a resourceFull pathway pipeline — quality control, normalization, clustering, differential expression, then GO / KEGG pathway enrichment via Enrichr with a barplot summary.
ChainLoad H5AD → Quality Control → Normalize → Log1p → Highly Variable Genes → PCA → Neighbors → Clustering → DEG Analysis → Pathway Enrichment → Pathway Visualization
Built aroundscrna/pbmc3k_raw.h5ad
SetupPathway Enrichment — install once
GO & KEGG Pathway Visualization
13 nodesgoKeggVisualizationTemplateNeeds a resourceChainLoad H5AD → Quality Control → Normalize → Log1p → Highly Variable Genes → PCA → Neighbors → Clustering → DEG Analysis → Pathway Enrichment → Pathway Visualization
Built aroundscrna/pbmc3k_raw.h5ad
SetupPathway Enrichment — install once
Cell-Cell Interaction (CellPhoneDB)
12 nodescellPhoneDBTemplateNeeds a resourceChainLoad H5AD → Quality Control → Normalize → Log1p → Highly Variable Genes → PCA → Neighbors → Clustering → CellPhoneDB → CellPhoneDB Plot
Built aroundscrna/pbmc3k_raw.h5ad
SetupCellPhoneDB — install once
LLM Auto Annotation
12 nodesautoAnnotationTemplateStandard clustering pipeline + LLM-based automatic cell type annotation. No reference atlas needed — the LLM predicts cell types from marker genes.
ChainLoad H5AD → Quality Control → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → UMAP → Clustering → Auto Annotation → Cluster Visualization
Reference Label Transfer
11 nodescellMapperTemplateTransfer cell type labels from a reference atlas to your query dataset using kNN-based mapping. Load both query and reference H5AD files.
ChainLoad H5AD → Quality Control → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → UMAP → Cell Mapper → Cluster Visualization
CellRank — CytoTRACE Fate Analysis
12 nodescellrankCytotraceTemplateCell fate analysis using the CytoTRACE kernel (developmental potential — no RNA velocity needed). Identifies terminal/initial states and computes fate probabilities.
ChainLoad H5AD → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → UMAP → Clustering → CellRank Kernel → Cell Fate Analysis → CellRank Visualization
CellRank — Pseudotime Fate Analysis
15 nodescellrankPseudotimeTemplateCell fate analysis using the DPT (Diffusion Pseudotime) kernel — combines pseudotime ordering with a Markov chain to compute fate probabilities.
ChainLoad H5AD → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → UMAP → Clustering → Diffusion Map → DPT → CellRank Kernel → Cell Fate Analysis → Lineage Drivers → Gene Trends
CellRank — Full Pipeline (CytoTRACE + All Viz)
16 nodescellrankFullTemplateComplete CellRank pipeline — transition kernel → cell fate → lineage drivers → every visualization (fate map, macrostates, gene trends).
ChainLoad H5AD → Normalize → Log1p → Highly Variable Genes → Scale → PCA → Neighbors → UMAP → Clustering → CellRank Kernel → Cell Fate Analysis → Lineage Drivers → CellRank Visualization → Gene Trends
04
Spatial transcriptomics (4)
Spatial Neighborhood Analysis
4 nodesspatialNeighborhoodTemplateNeighborhood graph and enrichment on Visium mouse brain sample
ChainLoad H5AD → Spatial Neighbors → Nhood Enrichment → Spatial Visualization
Built aroundspatial/visium_mouse_brain.h5ad
Spatial Autocorrelation Scan
4 nodesspatialAutocorrTemplateChainLoad H5AD → Spatial Neighbors → Spatial Autocorrelation → Spatial Visualization
Built aroundspatial/visium_mouse_brain.h5ad
Spatial Full Analysis
19 nodesspatialComprehensiveTemplateNeeds a resourceChainLoad H5AD → Highly Variable Genes → PCA → Neighbors → UMAP → Clustering → Spatial Neighbors → Nhood Enrichment → Co-Occurrence → Spatial Autocorrelation → Ripley → Ligand-Receptor → Spatial Visualization
Built aroundspatial/visium_mouse_brain.h5ad
SetupLigand-Receptor — install once
MERFISH / MERSCOPE Spatial Analysis
9 nodesmerfishSpatialTemplateChainLoad CSV / TSV → Add Coordinates → Merge Cell Meta → Normalize → Log1p → Spatial Neighbors → Spatial Autocorrelation → Spatial Visualization
05
Bulk RNA-seq (3)
Bulk RNA-seq Differential Expression
7 nodesbulkDifferentialTemplateLoad featureCounts output, run QC, normalization, DE, and volcano plot
ChainLoad featureCounts → Bulk QC → Filter Low Expressed → Bulk Normalize → Differential Expression → Bulk Visualization
Built aroundbulk/airway_featurecounts.txtbulk/airway_metadata.csv
Bulk RNA-seq with GSEA
9 nodesbulkGSEATemplateNeeds a resourceLoad Airway featureCounts, QC, filter, VST, DE, pathway enrichment (ORA), and heatmap
ChainLoad featureCounts → Bulk QC → Filter Low Expressed → Bulk Normalize → Differential Expression → GSEA / ORA → Bulk Visualization
Built aroundbulk/airway_featurecounts.txtbulk/airway_metadata.csv
SetupGSEA / ORA — install once
STAR ReadsPerGene — Single-sample Load Demo
3 nodesstarBulkDemoTemplateChainLoad featureCounts → Bulk QC → Bulk Visualization
Built aroundbulk/star_ReadsPerGene.out.tab
06
DNA methylation (Illumina array) (4)
Methylation — Full Pipeline (IDAT to DMP / DMR)
22 nodesmethylFullIdatTemplateNeeds a resourceChainLoad IDAT → Methyl QC → Methyl Normalize → Methyl Visualization → Methyl Filter → Merge Cell Meta → DMP Analysis → DMR Analysis → Methyl DMR Locus Plot → Methyl GOmeth
SetupLoad IDAT, Methyl GOmeth — install once
Methylation — Full Pipeline (Beta Matrix to DMP / DMR, EPIC v2)
15 nodesmethylFullBetaTemplateNeeds a resourceChainLoad Methyl Beta Matrix → Methyl Filter → Methyl Visualization → Merge Cell Meta → DMP Analysis → DMR Analysis → Methyl DMR Locus Plot → Methyl GOmeth
SetupLoad Methyl Beta Matrix, Methyl GOmeth — install once
Methylation — Visual EDA (no phenotype required)
4 nodesmethylVisualEDATemplateNeeds a resourceChainLoad Methyl Beta Matrix → Methyl Visualization
SetupLoad Methyl Beta Matrix — install once
Methylation — Pathway Discovery (Volcano + GOmeth)
8 nodesmethylPathwayDiscoveryTemplateNeeds a resourceChainLoad Methyl Beta Matrix → Methyl Filter → Merge Cell Meta → DMP Analysis → Methyl Visualization → Methyl GOmeth
SetupLoad Methyl Beta Matrix, Methyl GOmeth — install once
07
ATAC-seq (chromatin accessibility) (2)
ATAC Differential Accessibility
10 nodesatacDifferentialTemplateChainLoad ATAC MEX → ATAC QC → Peak Calling → LSI → Neighbors → UMAP → Clustering → Chrom Accessibility → ATAC Visualization
Built aroundatac/filtered_peak_bc_matrix
ATAC Motif Discovery
10 nodesatacMotifTemplateChainLoad ATAC MEX → ATAC QC → Peak Calling → LSI → Neighbors → Clustering → Chrom Accessibility → Motif Analysis → ATAC Visualization
Built aroundatac/filtered_peak_bc_matrix
About the “built around” data
22 templates were developed against a specific public dataset and carry sensible parameters for it; the remaining 11 — methylation, MERFISH, annotation and CellRank among them — open with the graph fully wired and the loader waiting for your own directory. Nothing is downloaded for you; see sample datasets for sources.