Transcriptomics & spatial biology
Compare gene expression and transcriptional programs across experimental conditions with an analysis matched to your study design.
Analyses are tailored to your experimental design, data quality and research question. The workflow and deliverables are agreed following project review.
Analysis modules
Read and sample quality control when primary sequencing files are supplied.
Alignment or transcript quantification and gene-level summarization.
Exploratory PCA, sample relationships and outlier assessment.
Differential expression with paired, longitudinal or covariate-aware designs.
Batch assessment and sensitivity analyses without removing the biological contrast.
Transcript usage and alternative splicing where the assay supports them.
RNA fusion analysis from suitable sequencing data.
Pathway enrichment, gene-set scores and immune signatures.
Reanalysis of compatible public cohorts with documented harmonization.
Data and metadata
Raw gene counts plus sample metadata for count-based differential expression; FASTQ for upstream computational processing; transcript abundance estimates through an appropriate import workflow.
Proposed deliverables
Quality-control report; expression matrices; effect sizes and adjusted statistical results; PCA, volcano and heatmap figures; pathway interpretation.
Interpretation and feasibility
TPM/FPKM values must not be passed directly to a count-based DESeq2 workflow. Biological replication and identifiable experimental contrasts must be assessed before inference.
Methods and references
Methods, reference resources and software are selected for each project after protocol, feasibility and licence review. Applicable versions, references and interpretation limits are documented in the agreed workflow.