Single-cell Transcriptomics Analysis
Single-cell Transcriptomics
For single-cell transcriptome data from platforms such as 10x Genomics, building a complete analysis pipeline from raw data to biological insights, including quality control, batch correction, dimensionality reduction, cell type annotation, differential expression, pseudotime trajectory inference, and cell-cell communication analysis.
Applicable Scenarios
- Tumor microenvironment cell atlas construction
- Immune cell development and differentiation research
- Drug response cell subpopulation identification
- Organ development and regenerative medicine
Data Inputs
- 10x Genomics scRNA-seq Fastq data
- Sample grouping info (treatment/control, time series, etc.)
- Known marker gene list (optional)
Deliverables
- UMAP / t-SNE dimensionality reduction visualization
- Cell subpopulation annotation and proportion statistics
- Marker gene expression heatmaps and violin plots
- Pseudotime trajectory and branch probabilities
- Cell communication networks (ligand-receptor pairs)
Analysis Pipeline
Cell Ranger
Alignment, quantification, feature-barcode matrix generation
QC & Filtering
Doublet removal, mitochondrial ratio filtering, gene count filtering
Normalization
LogNorm / SCTransform normalization
Integration
Harmony / Seurat CCA batch correction
Clustering
PCA → UMAP / t-SNE → Louvain/Leiden clustering
Annotation
Automated annotation + manual marker gene validation
Downstream
Differential expression, trajectory inference, cell communication, visualization
Related Case
Single-cell Transcriptomics Analysis
Established an integrated scRNA-seq and scATAC-seq multi-omics pipeline for large-scale immune cell data, building regulatory networks and identifying key cell subpopulations and mechanisms.
Deliverables
- Single-cell Integrated Analysis Pipeline
- Cell Subpopulation Annotation & Markers
- Regulatory Network Model
- Visualization Report