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Furthermore, in the top left, we note an abundance of genes with high average microarray intensities but low read coverage by sequencing.
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Microarray intensities should be normalized but not log-transformed before input into ISOpure, as the algorithm interprets each element as a normalized count of the number of copies of each transcript present in the sample.
Raw data such as sequence reads and unnormalized microarray intensities are not provided but are instead linked in the accompanying documentation to external repositories (e.g. GEO, ArrayExpress, SRA) where they can be retrieved.
There was variation in terms of the number of starting cells for each strain in the pool, but we normalized this by taking the ratio of the microarray intensities between the control and treated pools.
Microarray intensities were intra- and inter-array normalized using Limma package version 3.14.457 in the R statistical computing environment.
For visualization using heatmaps, normalized microarray intensities for each gene were further represented as z-scores among all cell types (Fig. S5).
Average normalized microarray intensities were analyzed using a two-sample, two-tailed t-test for unequal variances.
Microarray intensities were extracted using BeadStudio (Illumina) software.
The data set used consists of microarray intensities from the literature.
maSigPro was developed to treat continuous microarray intensities and applies LMs to model gene expression.
Furthermore, we suggest a method to correct for effects of RNA degradation on microarray intensities.
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