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I will analyze your single cell rna seq data and provide tailored insights
About this Gig
I offer custom single-cell RNA-seq analysis tailored to your dataset and research objectives. Each project is adapted to the biological question, applying reproducible workflows in R or Python (Seurat, Scanpy, etc.) to deliver high-quality and interpretable results.
Services include:
- Quality control and filtering
- Normalization, scaling, and dimensionality reduction (PCA, UMAP/t-SNE)
- Clustering and marker gene identification
- Optional cell type annotation and pathway enrichment
- Generation of publication-ready visualizations and summary reports
With over four years of experience in bioinformatics and multiple omics disciplines, I ensure each analysis is robust, transparent, and biologically meaningful. Please contact me before ordering to discuss your data and analysis goals.
My Portfolio
FAQ
What type of data can I provide?
You can provide either raw FASTQ files (for processing with Cell Ranger or equivalent) or processed count matrices (e.g., .mtx, .h5, or .csv format). I will adapt the workflow according to the data type and your project goals.
Do you perform raw data processing (FASTQ to count matrix)?
Yes. This step can be added as an extra service and includes read alignment, UMI counting, and generation of the expression matrix suitable for downstream analysis.
What tools do you use for the analysis?
I primarily use Seurat, Scanpy, Monocle3, and Cell Ranger, along with standard R and Python packages for visualization and enrichment analysis.
Can you integrate multiple samples or batches?
Yes. Batch correction and multi-sample integration can be included as an add-on. I use Seurat v4, Harmony, or Scanorama depending on the dataset characteristic
How do we discuss the biological question or analysis plan?
Before starting, we will exchange a few messages to define your main objectives and data details. I will then confirm the best package or suggest a custom offer if needed.
