Correlating gene expression profiles with CO2 treatment and OA-affected behaviours in the two-toned pygmy squid (Idiosepius pygmaeus)

• Other Authors: Celia Schunter (hasCollector), Jodi Thomas (hasCollector), Philip Laing Munday (hasCollector), Roger Huerlimann (hasCollector), Sue-Ann Watson (hasCollector), Timothy Ravasi (hasCollector)
• Format: Dataset
• Language: unknown
• Published: James Cook University
• Subjects: ocean acidification; carbon dioxide; transcriptomics; gene expression; central nervous system; eyes; squid; behaviour; Weighted Gene Co-expression Network Analysis; WGCNA; Canonical Correlation Analysis; neurobiological mechanisms; cephalopod; Ocean acidification
• Online Access: https://researchdata.edu.au/correlating-gene-expression-idiosepius-pygmaeus/2975056; https://researchdata.jcu.edu.au//published/cedbe9009e7a11ecadc7cd125c4cc081; https://doi.org/10.25903/7dcz-th66

Additional data for analyses correlating gene expression profiles in the nervous system with CO2 treatment and OA-affected behaviours of the two-toned pygmy squid (Idiosepiud pygmaeus). All R code used for the statistical analyses, and data files to accompany the statistical analyses (raw gene count data, CO2 treatment level and behavioural measurements for each individual squid, annotated transcriptome assembly of Idiosepius pygmaeus CNS and eye tissues, data linking each transcript from the transcriptome assembly to each gene in the gene count data). We correlated transcriptomic data from the central nervous system (CNS) and eyes of male two-toned pygmy squid (Idiosepius pygmaeus) with CO2 treatment levels and OA-affected behaviours in the same individuals to identify genes potentially contributing to OA-induced behavioural changes. We used Weighted Gene Co-expression Network Analysis (WGCNA) to cluster transcriptome-wide gene expression into modules. The gene expression profile of each module was then correlated with CO2 treatment levels (current-day: ~450 µatm, elevated: ~1,000 µatm) and visually-mediated behavioural responses previously shown to be altered at elevated CO2, using Canonical Correlation Analysis. All statistical analyses were carried out in R (v4.0.4), primarily using RStudio (v 1.4.1106). DESeq2 (v1.30.1) was used to normalise gene count data, remove low read counts and for variance stabilisation. WGCNA (v1.70-3) was used for network construction and module detection. CCA (v1.2.1) was used to explore the correlations between the two sets of variables from the same individual squid: set 1 = module eigengenes from each module representing the gene expression profile of the module, set 2 = CO2 level (current-day or elevated) and behavioural traits. WGCNA (v1.70-3) was used to calculate module membership and gene significance to identify hub genes. clusterProfiler (v3.18.1) was used to run over-representation analysis using the hypergeometric test. This data record contains: 1) All R code used ...