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To illustrate the use of summarized datasets for development, we created meta-profiles for mouse and Drosophila and clustered them using PCA.
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This summarized dataset S of clade counts was used for all further analyses.
The summarized dataset is available at http://prime.psc.riken.jp/?action=drop_index and as Additional File 4, Supplementary Data 3. Detailed information of extraction, MS conditions and data processing of GC-MS, LC-MS, CE-MS and IT-MS were performed as described in Chemical analysis metadata in the section of Metabolomics metadata.
We first summarized individual datasets using the MACCS representations and a single target (when activity data for more than one target was available).
The correlation scores were summarized between datasets derived from different individuals as shown (Additional file 2).
We summarized the datasets and categorized them by type of measure examined [ 24– 62] (Table 1).
Ordination by principal component analysis (PCA) resulted in a three-factor model, which efficiently summarized the dataset explaining 81.4% of total soil variance (TSV).
Authors are encouraged to summarize their datasets with descriptive statistics in the Technical Validation section, which should include the n value for each dataset; a clearly labelled measure of centre (such as the mean or the median); and a clearly labelled measure of variability (such as standard deviation or range).
Table 1 (see http://fcon-1000.projects //fcon-1000.projectss rsfMRI datasets.nitrc.org
In "Data for the FMT analysis" section, we summarized the dataset to estimate pore-fluid pressure.
Scatterplot Matrix (SPLOM) analysis and correlation of the methylation extent of DNA from different are summarized in dataset S2.
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