A structured single-cell transcriptomics system for reproducible cell-level analysis, interpretation, and reporting.
The Single-cell RNA-Seq System is included as a planned expansion within the CDI Omics Systems Architecture. It demonstrates how cell-level expression measurements can be transformed into biological insights through a structured and reproducible analytical framework.
Biological Focus
Single-cell RNA-Seq analysis enables the study of:
cellular heterogeneity
cell populations
cell states
developmental trajectories
marker genes
cell-type annotation
cellular interpretation
The goal is not simply to identify clusters of cells, but to understand the biological processes, cell types, and cellular states that drive observed patterns.
Why Single-cell RNA-Seq?
The Single-cell RNA-Seq System serves as the expansion architecture for cell-resolution transcriptomic analysis within the Omics Systems framework.
While bulk RNA-Seq evaluates average gene expression across samples, single-cell RNA-Seq enables the investigation of individual cells, cellular diversity, tissue composition, and cell-state transitions.
As a result, the Single-cell RNA-Seq System introduces analytical concepts such as cellular heterogeneity, cell-level quality control, dimensional reduction, clustering, marker detection, cell annotation, and marker-based interpretation while retaining the same principles of reproducibility, statistical reasoning, and biological interpretation.
Relationship to the Omics Systems Architecture
All Omics System Builds share a common analytical foundation.
Biological Question
↓
Experimental Design
↓
Data Generation
↓
Omics Data Processing
↓
Quality Control
↓
Feature Generation
↓
Domain-Specific Analysis
↓
Statistical Inference
↓
Biological Interpretation
↓
Reproducible Reporting
The Single-cell RNA-Seq System extends this architecture by transforming cell-level expression measurements into cellular evidence that can be explored, statistically evaluated, biologically interpreted, and reported within a reproducible analytical framework.
Single-cell RNA-Seq System Architecture
Code
flowchart TD A[Raw Counts] B[Cell and Gene QC] C[Normalization] D[Dimensional Reduction] E[Clustering] F[Marker Detection] G[Cell Annotation] H[Biological Interpretation] I[Reproducible Reporting] A --> B B --> C C --> D D --> E E --> F F --> G G --> H H --> I style A fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#0f172a style B fill:#e0f2fe,stroke:#0284c7,stroke-width:2px,color:#0f172a style C fill:#ecfeff,stroke:#0891b2,stroke-width:2px,color:#0f172a style D fill:#ede9fe,stroke:#7c3aed,stroke-width:2px,color:#0f172a style E fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#0f172a style F fill:#fae8ff,stroke:#c026d3,stroke-width:2px,color:#0f172a style G fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#0f172a style H fill:#ecfccb,stroke:#65a30d,stroke-width:2px,color:#0f172a style I fill:#f0fdf4,stroke:#16a34a,stroke-width:2px,color:#0f172a
flowchart TD
A[Raw Counts]
B[Cell and Gene QC]
C[Normalization]
D[Dimensional Reduction]
E[Clustering]
F[Marker Detection]
G[Cell Annotation]
H[Biological Interpretation]
I[Reproducible Reporting]
A --> B
B --> C
C --> D
D --> E
E --> F
F --> G
G --> H
H --> I
style A fill:#dbeafe,stroke:#2563eb,stroke-width:2px,color:#0f172a
style B fill:#e0f2fe,stroke:#0284c7,stroke-width:2px,color:#0f172a
style C fill:#ecfeff,stroke:#0891b2,stroke-width:2px,color:#0f172a
style D fill:#ede9fe,stroke:#7c3aed,stroke-width:2px,color:#0f172a
style E fill:#f3e8ff,stroke:#9333ea,stroke-width:2px,color:#0f172a
style F fill:#fae8ff,stroke:#c026d3,stroke-width:2px,color:#0f172a
style G fill:#fef3c7,stroke:#d97706,stroke-width:2px,color:#0f172a
style H fill:#ecfccb,stroke:#65a30d,stroke-width:2px,color:#0f172a
style I fill:#f0fdf4,stroke:#16a34a,stroke-width:2px,color:#0f172a
System Components
Input Data and Metadata
Single-cell RNA-Seq analysis begins with expression measurements linked to cell-level, sample-level, and experimental metadata.
Common inputs include:
raw count matrices
cell barcode files
gene or feature annotation files
sample metadata
experimental condition metadata
batch or sequencing-run information
Cell and Gene Quality Control
Quality control evaluates cell integrity, sequencing depth, feature counts, and potential technical artifacts before downstream analysis.
Common assessments include:
number of detected genes
sequencing depth
mitochondrial content
ribosomal content
doublet detection
outlier detection
low-quality cell filtering
Normalization
Normalization adjusts expression measurements to improve comparability across cells while reducing technical variation.
This step prepares the data for dimensional reduction, clustering, marker detection, and downstream biological interpretation.
Dimensional Reduction
Dimensional reduction simplifies high-dimensional expression data into lower-dimensional representations that support exploration and visualization.
Common approaches include:
PCA
UMAP
t-SNE
Clustering
Clustering identifies groups of cells with similar expression profiles.
These clusters often represent distinct cell populations, cell states, developmental stages, or condition-associated cellular patterns.
Marker Detection
Marker detection identifies genes that distinguish clusters and support downstream biological interpretation.
Marker genes provide evidence for cell-type assignment, state characterization, and biological interpretation.
Cell Annotation
Cell annotation assigns biological meaning to clusters using marker genes, reference datasets, curated knowledge, and domain expertise.
This step should be treated as an evidence-based interpretation process rather than a purely automated label assignment.
Biological Interpretation
Biological interpretation connects observed cellular patterns to biological processes, developmental programs, tissue organization, immune states, environmental responses, or disease mechanisms.
Reproducible Reporting
Reproducible reporting connects analytical decisions, quality thresholds, visualizations, marker evidence, annotation logic, interpretation, and conclusions within a transparent analytical document.
Typical tools include:
Quarto
GitHub
reproducible computational environments
Core Technologies
Examples of technologies commonly used within the Single-cell RNA-Seq System include:
Seurat
Scanpy
SingleCellExperiment
scater
scran
UMAP
PCA
Quarto
GitHub
These technologies support the workflow, but the primary focus of the Single-cell RNA-Seq System is cellular reasoning, marker-based interpretation, and reproducibility.
Expected Outputs
A complete Single-cell RNA-Seq System should produce:
cell and gene quality control summaries
filtered expression matrices
normalized expression objects
dimensional reduction outputs
clustering results
marker gene tables
cell annotation evidence tables
cell-type or cell-state summaries
biological interpretation summaries
reproducible analytical reports
Status
Planned expansion
The Single-cell RNA-Seq System is included as an expansion architecture for cell-resolution transcriptomic analysis within the CDI Omics Systems framework.
The Single-cell RNA-Seq System illustrates the Omics Systems approach to cell-level transcriptomic analysis.
Rather than treating quality control, normalization, clustering, annotation, interpretation, and reporting as separate activities, the system connects them into a unified analytical framework.