As a popular brand management practice, firms communicate with consumers using images on social media platforms such as Instagram. Image-based brand communications on social media have two important characteristics. First, brand communications on social media are bilateral, not one-sided as those on traditional media. Second, measuring the information an image delivers is challenging. This study conceptualises a bilateral brand communication model to capture image information exchange between firms and consumers. This research examines how image information generated by firms and consumers changes in such dynamic and dyadic brand communications on Instagram and how brand communication strategies may alter the image information gap between the two parties. This study defines image information richness (IIR) based on information richness theory and applies machine learning techniques to measure IIR through the objective cues present in images. This approach provides brand managers with a practical tool to diagnose and manage their image-based brand communications. Analysis of 10,765 firm-generated images and 6,689 consumer-generated images from eight digital camera brands on Instagram reveals positive dynamic effects but asymmetrical dyadic effects of IIR. Consumer IIR can be inconsistent with firm IIR and thus form an IIR gap.

SUMMARY: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/software.htm CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Systematic information integration of multiple-related microarray studies has become an important issue as the technology becomes mature and prevalent in the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and considering expression pattern concordance are rarely explored. RESULTS: In this article, we develop three integration methods for biomarker detection in multiple multi-class microarray studies: ANOVA-maxP, min-MCC and OW-min-MCC. We first consider a natural extension of combining P-values from the traditional ANOVA model. Since P-values from ANOVA do not guarantee to reflect the concordant expression pattern information across studies, we propose a multi-class correlation (MCC) measure to specifically seek for biomarkers of concordant inter-class patterns across a pair of studies. For both ANOVA and MCC approaches, we use extreme order statistics to identify biomarkers differentially expressed (DE) in all studies (i.e. ANOVA-maxP and min-MCC). The min-MCC method is further extended to identify biomarkers DE in partial studies by incorporating a recently developed optimally weighted (OW) technique (OW-min-MCC). All methods are evaluated by simulation studies and by three meta-analysis applications to multi-tissue mouse metabolism datasets, multi-condition mouse trauma datasets and multi-malignant-condition human prostate cancer datasets. The results show complementary strength of the three methods for different biological purposes. AVAILABILITY: http://www.biostat.pitt.edu/bioinfo/. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.