I will do single cell rna seq analysis

Need a reliable, publication-ready single-cell RNA-seq (scRNA-seq) analysis? I will run a complete, reproducible pipeline that goes from raw counts to publishable figures.

I am a PhD researcher with peer-reviewed publications in Q3-Q4 SCI journals and hands-on experience with 10x Genomics scRNA-seq on human, mouse, and plant samples. Every project ships with versioned R or Python scripts, a written methods paragraph, and figures that pass journal figure checks (300+ dpi, vector text where possible).

What I deliver, end-to-end:

- Quality control: cell/gene filtering, doublet detection, mitochondrial/ribosomal filtering, before/after QC plots

- Normalization and integration: Seurat SCTransform or Scanpy normalization; Harmony, Seurat v5, or Scanorama for multi-sample / batch

- Dimensionality reduction and clustering: PCA, UMAP/t-SNE, resolution tuning with clustree

- Cell type annotation: marker-based, plus reference mapping with SingleR, Azimuth, or CellTypist; plant annotation via PlantPhoneDB or scPlantLLM

- Differential expression: cluster markers and condition vs condition, with volcano / MA / heatmap / dotplot

- Advanced analyses (Standard & Premium): trajectory inference