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I will analyze human single cell rna seq data with cell annotation
Bioinformatics Analyst RNA seq Scientific Data Visualization
Bioinformatics analyst specializing in human bulk RNA-seq, gene expression analysis, and scientific data visualization. I help researchers turn count matrices and metadata into clear, reliable results...
About this Gig
Turn your human single-cell RNA-seq data into clear, structured, and publication-ready biological insights.
I provide a careful, identity-aware analysis workflow that preserves the relationship between cells, samples, and subjects throughout the project. Your results will be delivered with high-quality figures, organized tables, and clear scientific interpretation.
Depending on your selected package, the analysis includes:
Count matrix and metadata validation
Per-sample quality control
Doublet detection and filtering
Normalization, PCA, UMAP, and clustering
Integration and batch-effect review
Marker gene identification
Evidence-based cell type annotation
Publication-ready UMAP, dot plots, heatmaps
Organized H5AD files and result tables
Subject-aware pseudobulk differential expression
Sample-level cell composition analysis
Structured analysis report and quality records
This service is ideal for human multi-sample single-cell studies in biomedical, pharmaceutical, and biotechnology research. I combine bioinformatics analysis with scientific visualization to provide results that are clear, reliable, and ready for research reports, presentations, and publication preparation.
My Portfolio
FAQ
What input data do you accept?
I work with a human integer gene-by-cell count matrix and matching metadata. Please provide cell-to-sample mapping, subject IDs, condition labels, and gene identifiers where available.
Do you process raw FASTQ files?
These packages begin from an integer count matrix. Raw FASTQ alignment and quantification are not included. Please contact me if your data are currently at an earlier processing stage.
How do you perform cell type annotation?
I use marker genes, cluster expression patterns, and available biological references. Annotation is evidence-based, and uncertain or conflicting populations are clearly identified for review.
How is differential expression analyzed?
When the study design supports it, the Premium package uses subject-aware pseudobulk analysis. Cells provide measurement depth, while biological subjects define statistical replication.
Are reproducible analysis scripts included?
R or Python scripts and notebooks are not included by default. Reproducible scripts can be purchased as a paid Extra after the scope, data format, and required environment are reviewed.
Can you guarantee specific markers or significant results?
Results depend on data quality, study design, biological variation, and sample replication. I provide a rigorous and transparent analysis, but specific markers or statistical significance cannot be guaranteed.
What analyses are not included in these packages?
Spatial transcriptomics, scATAC-seq, multiome, trajectory, cell-cell communication, regulatory-network, and CNV analyses require a separate feasibility and pricing review.
