We offer analysis of all high-throughput data types, including:
- Gene expression data from microarrays, qPCR, and RNA-seq
- Proteomics, phosphoproteomics, metabolomics and lipidomics from mass spectrometry and SomaLogic
- Phylogenetic and metagenomics analysis of DNA sequences
- DNA methylation sequencing data
The typical pipeline includes: normalization, quality control, Principal Component Analysis (PCA), differential expression, pathway analysis, and visualization. A visualization example is shown in Figure 1.
Figure 1: Gene expression heatmap with UCP1 color bar from Xue et al. (2015)
- Sample size and power calculations for high-throughput studies
- State-of-the-art reproducible workflows
- Analysis and meta-analysis of public data
- Novel network analysis methods
- Integration of multiple data types, including clinical covariates
- Causal inference testing (AKA mediation analysis)
- Global metabolic flux inference from Seahorse assays
- An in-house searchable gene expression database with >75 studies (output from two studies for searching a gene of interest is shown in Figure 2)
Figure 2: Joslin Gene Expression Database Profile Example. Users can search for any gene of interest, and retrieve the profiles for that gene across all studies, with fold-change (FC), p-value (P), and Benjamini-Hochberg (BH) false discovery rate per comparison shown at bottom.