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We analyzed two publicly available, longitudinal human microarray datasets that describe self-resolving immune responses.
We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process.
Finally, a recent study used three PD microarray datasets that were not used in our study [59], [60], [61] and identified the splicing factor SRRM2 as the only gene that was dysregulated in PD in all three datasets [62].
We collected all publicly available microarray datasets that corresponded to pig immune response studies.
29 microarray datasets that corresponded to 809 chips were considered in this study.
Given a set of query genes, SPELL identifies the expression microarray datasets that are most informative for these genes.
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A total of 4021 genes were included in the final microarray dataset that contained the M-CGH profiles of the 174 strains.
From the observation, we filters almost 75% genes from each microarray dataset that means microarray technology can measure thousands of genes simultaneously, but it also contains much noise that causes a lot of missing values.
Table 1 summarises the characteristic of datasets used in this study.> In this analysis, we analysed an RNA-seq dataset of Gata3-KO and WT T cells including Th1, Th2, Th17, and iTreg (GSE20898 [ 24], designated as the Gata3 dataset) and a microarray dataset that analysed the same Th subsets from WT mice (GSE14308 [ 23], designated as the Th dataset).
The microarray dataset that we used is the one described in section "Performance on the Spellman Dataset".
To identify FLcDNAs that were DE following FTC feeding, FLcDNAs mapping to the microarray were matched to an existing microarray dataset that examined gene expression in hybrid poplar leaves 24 hours after continuous FTC feeding ([ 11]; GEO series number GSE9522).
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