Single-Cell RNA Sequencing for Cell Cycle Factor Validation

A cutting-edge research team investigating cellular mechanisms faced computational challenges in analysing their complex single-cell RNA sequencing (scRNA-seq) data. They needed to validate hypotheses about cell cycle-related factors and investigate an observed bimodal distribution in mitochondrial gene expression, while addressing inherent data sparsity issues.

• Validate cell cycle-related factors in single-cell data

• Investigate the biological significance of mitochondrial bimodality

• Address technical challenges in sparse scRNA-seq data

• Develop publication-quality visualisations

• Provide comprehensive biological interpretations of all identified factors

1. Data Architecture Analysis

• Implemented pseudo-bulking methodology to address data sparsity

• Applied strategic data filtering protocols

• Mapped technical and biological variation sources

• Designed reproducible analytical workflow

2. Technical Implementation

• Processed aggregated counts through DESeq2 with variance stabilising transformation

• Performed dimensionality reduction techniques (PCA, UMAP)

• Conducted differential expression analysis between cell clusters

•  Implemented quality control measures to identify technical artefacts

3. Solution Delivery

• Comprehensive Jupyter notebooks with reproducible code

• Publication-quality visualisations

• Biological interpretation of identified factors

• Clear recommendations for downstream analyses

Quantitative Outcomes

• Clear identification of distinct cell cycle phase groupings

• Validation of differential expression with stringent statistical thresholds (0.1% FDR)

• Identification of cluster 17 as technical artefact rather than biological phenomenon

• Confirmed enrichment of cell cycle pathways in differentially expressed genes

Qualitative Benefits

• Enhanced understanding of technical vs. biological variation

• Clear pathway for excluding low-quality cells

• Framework for investigating guide-cell cycle interactions

• Robust validation of biological signals against technical noise

"Umbizo was a pleasure to work with. The team has excellent attention to detail, approaches tasks thoughtfully, and delivers thorough, high-quality work. I highly recommend them to anyone looking for reliable and meticulous support."

Jacob Ojoie - Principal Investigator

• Initial Assessment and Planning: 1 day  

• Initial Analysis Completion: 7 days

• Advanced Analysis Delivery: 9 days

• Final Delivery: 11 days

Expanding analytical framework to explore interactions between cell guides and cell cycle progression, developing new visualisation techniques for complex single-cell data, and supporting publication preparation with robust biological interpretations.