In these troubled times there are many of you scientists making valuable and tremendous efforts to find answers and solutions. Data is rapidly being generated and published with high frequency to gain increasing insights into the biology of COVID-19.
Qlucore Omics Explorer is an excellent tool when time is critical. Qlucore's visualization and unique software architecture enables faster and more visual analysis.
COVID-19 related data sets are increasingly becoming available in public repositories such as the Gene Expression Omnibus (GEO). One example is the data set GSE14507.
The authors write "Here, we offer the first in-depth characterization of the host transcriptional response to SARS-CoV-2 and other respiratory infections through in vitro and ex vivo model systems. The overall design: Independent biological triplicates of primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells were mock treated or infected with SARS-CoV-2 (USA-WA1/2020) at different MOI (NHBE: 2, A549: 0.2). Additionally, independent biological duplicates of A549 cells were Mock treated or infected with RSV (A2 strain, MOI 15) or IAV (A/Puerto Rico/8/1934 (H1N1, MOI 5))."
This and other data sets are quickly and easily loaded into and analyzed in Qlucore Omics Explorer. The PCA plot on the top was generated using the GSE14507 data set. It was imported with the Wizard in Qlucore and genes were reduced by identifying discriminating genes, between the groups SARS004-CoV2 and SARS004-mock (t-test, q-value = 0.1), retaining 88 genes, i.e. the transcriptional signatures caused by the virus. Each sphere is a sample and The PCA components in the plot are calculated on the values for these 88 genes in the SARS004-CoV2 and SARS004-mock groups. Samples from other groups are added to the plot for visualization.
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