The Biostatistical Tool for Gene Expression Data Analysis
an easy-to-use web server for high-throughput gene expression analysis a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do currently
provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications.
Supported Transcriptome data:
RNA-Seq and Microarray
GeneCloudOmics performs raw data normalization using four normalization methods RPKM,
FPKM, TPM and RUV. The raw vs. normalized data are visualized as boxplots and violin plots.
Differential Gene Expression (DGE) Analysis:
GDE using five methods EdgeR, DESeq2 and NOISeq.
GeneCloudOmics provides the user with the following bio-statistical analyses:
Pearson and Spearman rank correlations, PCA, k-means and hierarchical clustering,
Shannon entropy and noise (square of the coefficient of variation), t-SNE, random forest and SOM analyses. All analyses include proper high-resolution visualization.
Bioinformatics Analysis of Gene and Protein sets:
For the differential expressed genes (DEG), GeneCloudOmics provides
the users with multiple bioinformatics tools to investigate their
gene/protein list including gene ontology (GO), pathway enrichment analysis, PPI, co-expression, gene/protein function, subcellular localization, complex enrichment, protein domains, tissue expression, sequence properties (acidity, hydrophobicity and charge),
evolutionary analysis (gene tree, phylogenetic tree and species/chromosome location) and pathological analysis (diseases that these genes/proteins are involved in). The analyses include proper high-resolution visualization, when applicable.