Thomas Sirchi

Computational Biologist · Cancer Genomics · Single-Cell & Multi-Omics

About me

I am a computational biologist working in cancer genomics, using high-dimensional molecular data to investigate how tumour cells change state, interact with their microenvironment, and respond to treatment.

My work combines transcriptomics, single-cell biology, multi-omics integration, statistics, and machine learning, with experience across lung cancer and haematological malignancies and an emphasis on reproducible analysis and biologically interpretable results.

Research focus

Cancer ecosystems & plasticity

Tumour heterogeneity, malignant state transitions, metastatic progression, treatment response, and tumour–microenvironment interactions.

Single-cell, spatial & multi-omics

Single-cell transcriptomics, spatial molecular data, and integration of transcriptomic, genomic, and epigenomic measurements.

Computational methodology

Statistical genomics, machine learning, explicit quality control, batch-effect modelling, and reproducible computational workflows.

Computational toolkit

Languages & workflows

R Python Bash Nextflow

Data analysis

scRNA-seq Spatial transcriptomics Bulk RNA-seq miRNA-seq Multi-omics Differential expression Pathway analysis Immune deconvolution

Approach

Reproducible workflows Statistical modelling Machine learning Data visualisation Biological interpretation

I build reproducible computational workflows for sequencing QC, statistical analysis, integrative modelling, and biological interpretation.

Current focus: tumour heterogeneity, cell-state plasticity, and metastatic progression through single-cell and multi-omic approaches.

Software

veryMAD

Explicit MAD-based quality control and diagnostic visualisation for high-dimensional biological data.

R QC MAD

View repository

veryMADpy

Python/AnnData implementation of explicit MAD-based quality control for modern single-cell workflows.

Python AnnData Scanpy

View repository

ComBat-refQL

Reference-batch adjustment for bulk RNA-seq count data using quasi-likelihood modelling and explicit diagnostics.

R edgeR RNA-seq

View repository

simple-nextflow-salmon

Lightweight reproducible Nextflow workflow for RNA-seq quantification with Salmon and integrated QC.

Nextflow Salmon RNA-seq

View repository

See all software projects

What I care about

Biological question Statistical evidence Interpretable biology

I am particularly interested in computational approaches that connect molecular measurements with tumour cell state, evolutionary dynamics and phenotype, rather than treating high-dimensional data as an endpoint by itself.