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Firstly, pre-processed Affymetrix datasets were collapsed to the probeset with the maximum expression value for each gene using the Gene Set Enrichment Analysis software (details below).
Datasets were collapsed to the maximum probe level per gene using GSEA, filtered to those genes which were present across both platforms and ranked using the GSEA metric "difference of class means".
Datasets were collapsed from probes to gene symbols using default settings, and 1000 permutations were conducted.
All raw datasets were collapsed to a one probe per gene level using the R function collapseRows [ 73].
For cross-platform comparisons, datasets were collapsed to single genes based on highest mean average and mapped based on Unigene ID.
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GSEA setting applied in this study: dataset was collapsed to gene symbols and 1000 permutations were run (Permutation type: Gene Set).
To study the base composition and thermodynamic stability, the reads were collapsed to non-redundant datasets.
The isoforms with only terminal differences at the 3' and 5' ends were collapsed by running the dataset through CAP3 assembler [42].
When collapsing probes to genes for each dataset, probes with the same Entrez IDs were collapsed to the maximum mean expression per gene using an empirically recommended method [ 46] and the Bioconductor annotation package biomaRt (v2.18.0), resulting in a set of 8442 human Entrez genes measured across all cohorts.
The over 30 reasons for a psychiatric referral were collapsed into 10 logical groupings (B dataset) for purposes of analysis.
Centroid genes were mapped onto our dataset by gene symbol; and genes represented by multiple probes were collapsed by averaging.
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