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Contact Shirshak A.

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USD 25 /hr

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Hire Shirshak A.

Nepal

USD 25 /hr

Entry-Level scRNA-seq Analyst | Python & Scanpy

Profile Summary

I am a Computing graduate (First Class Honors) who focuses on turning scRNA-seq count matrices into clear, intuitive biological insights. I specialize in the Scanpy/AnnData ecosystem and believe that a project isn't finished until the results can be explained simply.
Technical Focus:

• scRNA-seq Downstream Workflow: Full processing from raw counts, including QC (filtering/normalization), Dimensionality Reduction (UMAP), and Clustering.
• Functional Analysis: Differential expression followed by Pathway Enrichment Analysis to translate gene lists into biological meaning.
• Perturb-seq Experience: Hands-on analysis of the Norman et al. (2019) K562 dataset, specifically tracing CEBPE-driven granulocytic differentiation.

My Approach: Intuition-Driven Analysis
I am a "slow and deep" learner. My philosophy is inspired by the Feynman technique: I do not move past a concept or a data trend until I truly understand its underlying logic. This means I don't just "run a pipeline"—I ensure every filter and cluster makes sense.
I deliver analysis via well-documented Jupyter Notebooks that bridge the gap between complex computational data and clear biological stories.
While I am new to professional freelancing, I bring significant technical rigor, evidenced by my research into scRNA-seq GRN inference methods which achieved a 95.7% precision improvement over existing models. I am looking for roles where I can help researchers find the "signal" in their scRNA-seq data.

Subject Matter Expertise

Services

Data & AI

Data & AI Data Visualization, Data Cleaning, Data Processing, Data Insights

Work Experience

Independent

April 2026 - Present

Education

BSc (Hons) (First Class) Computing

London Metropolitan University

February 2023 - December 2025

Certifications

Certification details not provided.

Publications

WORKING PAPER

AENetMoX: Fast and Precise scRNA-seq Gene Regulatory Network Inference Through Multi-Modal Feature Integration in Brain Organoids .