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This plot was generated using the Bioconductor library affyQCReport [ 11].
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In the present study we evaluated Support Vector Machines, Diagonal Discriminant Analysis (Diagonal Linear Discriminant Analysis or DLDA and Diagonal Quadratic Discriminant Analysis or DQDA), Random Forests and k Nearest Neighbours using the Bioconductor libraries e1071, ipred, randomForest and class, respectively.
Data was analysed using the Bioconductor libraries 'oligo', 'limma' and 'made4' [ 28– 30].
Final data analysis and visualisation of results was done with custom R scripts using the Bioconductor libraries [ 49] when possible.
To facilitate microarray analysis using the Bioconductor affy library [25], the transcript-specific probe set definitions were stored in a CDF file using the Bio::Affymetrix::CDF perl module [26].
The data from the final analysis was normalized using the bioconductor [76] library "limma" [77] and the non-print tip lowess approach without background subtraction [78] as this increases the accuracy of the obtained measurements [68].
The 44 775 probes were assigned to 12 494 Entrez genes using the Bioconductor Affymetrix Human Genome U133A 2.0 Array annotation library.
Differential expression in poly(A) selected libraries between wild-type and tdp-1 ok803 tdp-1 ok803 calculated using the Bioconductor package edgeR (release 2.12) (Robinson et al, 2010).
Official gene symbols for probe sets were obtained using the Bioconductor annotation database mouse4302.db.
All subsequent analysis was performed using the Bioconductor software environment.
Microarray data were then analyzed using the Bioconductor Limma package.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com