Gene Expression Analysis

Gene expression analysis gives us a snapshot of a sample’s transcriptome, providing insight into the composition and activity of cells in that sample. Most gene expression data analyses are designed to quantify changes in expression between groups of samples that differ with respect to some treatment, experimental condition or outcome.

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How it works

Lead Science offers established, cost-efficient and rapid turnaround analysis services for gene expression data from a range of platforms including bulk RNAseq, array and NanoString, as well as CRISPR knockout data.

For RNAseq data, we offer analyses of not only mRNA but also miRNA and other small RNAs if required.

We are able to receive data in various formats for analysis such as raw FastQ files, aligned BAM/SAM files for Next Generation Sequencing (NGS) data, and raw or normalised (FPKM/TPM) count matrices at gene or transcript level. Platform-specific file formats such as CEL for arrays (e.g. from Affymetrix, Illumina, Agilent) or nCounter (RCC files) for NanoString are also routinely handled.

Gene expression analyses are relevant for a range of applications such as:

  • Target identification and validation in drug discovery
  • Identification of novel biomarkers or gene signatures associated with drug response or survival
  • Profiling of normal vs. diseased tissue

Analyses of this kind are applicable to a very broad range of therapeutic areas including (but not limited to) oncology, cardiovascular and metabolic disease.

 

Interested in Gene Expression Data Analysis?

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Learn more about our Gene Expression Analyses

How it works

  • Quality control evaluation of raw gene expression data.
  • Alignment and/or quantification using standard bioinformatics tools (e.g. STAR, TopHat, salmon etc) as required.
  • Normalisation across samples using trimmed mean of M-values normalisation (TMM).
  • Analysis of sample metadata to identify, for example, associations between biological and technical variables.
  • Exploratory data analysis and evaluation using unsupervised clustering and dimension reduction techniques to assess overall sample quality and identify possible outliers or batch effects.
  • Differential expression analysis with a range of tools (e.g. voom/limma, DESeq2, EdgeR).
  • Functional enrichment analysis using curated resources
    such as the Reactome pathway database and the Gene Ontology (GO) resource.

We also provide similar pipelines covering all array platforms which can be adjusted to apply the most appropriate techniques in any situation, e.g. Robust multi-array average (RMA) normalisation for Affymetrix data.
Furthermore, if required, we can include a range of further bolt-on analyses, for example gene set enrichment
analysis (GSEA) to assess functional enrichment.

Every time our clients work with us, they benefit from:

  • A dedicated analyst backed by an experienced team to curate all data, identify the most appropriate statistical approach to take and provide a biological interpretation of results.
  • An interactive data analysis report, internally peer-reviewed, including all analysis methods and results.
  • Post-report follow ups: upon receipt of our data analysis report, we arrange a teleconference so that our lead analyst can talk through the results.
  • Access to large capacity computing and secure data storage facilities.

Below is a small selection of gene expression data analysis projects where we have successfully helped our clients:

  • Analysing expression profiles of drug-resistant cell lines.
  • Assessing the effect of a gene knockout on expression in cancer cell lines.
  • Performing unsupervised clustering of expression data to stratify samples.
  • Associating drug response with changes in expression profiles to identify potential mechanisms of response to treatment.
  • Analysing expression of diseased vs. normal samples to gain insight into potential pathological mechanisms.
  • Investigating the effect of drug treatment on the expression profile of a cancer cell line over a time course.
  • Identification of gene signatures associated with response to treatment.
  • Multivariate analysis across RNAseq and SNP/CNV data, alongside efficacy data from over 800 CRISPR screens.  
  • Association of gene expression or mutations that confer sensitivity to CRISPR gene knockout in cell lines of interest. 

How it works

Lead Science can help with a range of CRISPR-generated data such as:

  • Gene expression and GWAS
  • Epigenetics
  • Microbiome
  • Metabolomics and proteomics

We can analyse data taken from publicly available data sources in addition to datasets provided by clients.

We have worked on a range of related projects including:

  • Multivariate analysis across RNA-Seq and SNP/CNV data, alongside efficacy data from over 800 CRISPR screens.
  • Identification of sensitivities to CRISPR knockouts in cell lines of interest.

We were very pleased with the service provided by  Lead Science. In particular, the Lead Science team were flexible and willing to spend time understanding our specific project needs and the key scientific questions we were asking of the experiments. They tailored the data output in a way that specifically addressed these questions, which was really helpful. The data package was Web-based and interactive and we were delighted with the way it was presented and explained to us. We will certainly work again with the Lead Science team for any future projects.