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RNA-Seq dataset and preprocessing (Dataset I).
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Yu et al. preprocessed dataset GSE3068.
Similar to the analysis of idiopathic and LRRK2 PD patients, we used preprocessed datasets to build PLS-DA models for class separation and membership prediction.
First, the system enables its users to import preprocessed datasets by uploading a matrix of normalized transcript expression data containing rows representing individual transcripts and columns representing samples (individual microarrays).
The collected and preprocessed datasets were decomposed into binary comparisons between two conditions, denoted by A and B, to be able to determine differentially expressed genes and gene sets.
After preprocessing, the dataset contains 8305 journals categorized in 22 scientific fields.
It is generally difficult to directly compare the results of different studies since there are often differences in partitioning and preprocessing the datasets for training and testing, as shown in the studies by Pang et al. [4].
What we have described so far are more or less standard procedures for preprocessing NGS datasets.
In addition, a flow chart summarizing the procedure used for preprocessing our datasets is provided in Supplementary Figure S3.
Lin Zhu collected the experimental datasets and preprocessed the datasets.
Taking a pragmatic approach, we preprocessed the dataset in the following way: (1) We removed all patient records that have a missing value in any of its dimensions.
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