Our clients often come to use with an existing data set coming from a predefined experimental set-up. Also, often TenWise is involved in designing such experiments.
In both cases we perform standard QA/QC steps on incoming data and subsequently perform detailed statistics on the data. Finally we perform state-of-the-art data analysis on the data sets helping our customers towards a new value inflection point.

Exploratory gene expression analysis

We start our analysis with a quality control step using PCA plots. With this analysis the largest differences between samples can be shown via clustering and differences between (experimental) or biological conditions can be found. Overall, we answer the question: do we trust the experimental outcomes sufficiently to continue?

 

Figure 1: PCA plot example displaying different groups as clusters

 

Differential expression analysis

Differential Expression Analysis is a statistical comparison of two sample groups. It results in differential expression statistics for each detected transcript, such as the fold change and statistical significance. These statistics are typically visualized using a volcano plot.

The plot displays ‘Differentially Expressed Genes’ by discerning between samples that are eg. ‘down regulated’, ‘not affected’ and ‘upregulated’. TenWise also connects found genes of interest to what is known in literature hereby answering the question: Can we corroborate gene names with existing knowledge (literature)?

Next, we zoom in on specific expression profiles using box plots that show high occurrence in certain samples versus low occurrence in other samples.

Figure 2: Volcano plot also displaying curated single gene names that are of interest

 

Figure 3: Aim is to zoom in on specific expressions (eg. SKIDA1) that are expected to play a role in disease (eg. leukemia), from: Lopes et al. , 2022.

GO term enrichment analysis

Go Term Enrichtment analysis puts the found genes in relation to biological meaningful terms especially pathways such as signaling pathways, metabolic pathways and molecular pathways. The analysis helps to discern between biological pathways (GO terms) per condition (A versus B)

 

Figure 4: This Go Term Enrichment analysis (Yang et al, 2018)

Transcriptomics – Gene expression data in single cells

Single-cell RNA-sequencing CIBERSORT experiments are about in silico flow cytometry. This is used to determine cell type fractions in bulk gene expression data.

Typical inputs are a signature matrix (e.g. Lm22) and bulk RNAseq data leading to outputs being cell type fractions per sample as depicted below:

Figure 5: Workflow for CIBERSORT analysis as described by Stanford labs.

Figure 6: CIBERSORT data showing that proportion of CD8+ T cells and M2 macrophages were significantly increased in the LARC microenvironment after CRT.