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We first verified that there are intensity-dependent array effects and effect due to the two batches.
The probe intensities for the third simulation scenario included biological effects, array effects and intensity-dependent dye effects as described above.
Meanwhile the standardized estimates of differential expression (y i ) are adjusted for all other sources of variation fitted in the within-study model (i.e. dye and array effects), and standardized by the standard error of the estimate.
To apply SNM, all terms in the model were defined exactly as in the first scenario, except we now defined f 1(BX + AZ) to be a 100 000 × 12 matrix that represents the intensity-dependent array effects and f 2(BX + AZ) to be a 100 000 × 12 matrix that represents the intensity-dependent dye effects.
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In these situations, a direct-comparison design helps to maximize signal-to-noise ratios in the ratios between the two groups through minimization of the array effect and the possibility for more replicates with the same number of arrays.
Model 1 represents the ratio-based analysis: (1) Here, μ captures the average gene intensity, τ i is the treatment specific effect, η j is the cell line (10 or 19) effect, α k (j ) is the array effect, and ε ijk is the error component.
Affymetrix raw data files (cell intensity [CEL] files) were first analyzed with robust multi-array Average (RMA) normalization as implemented in the Affymetrix Expression Console Software (version 1.1) to remove between-array effects and to standardize the low-level data [ 62].
Data from a total of 82 microarrays (38 PAR and 44 MFP; 41 samples from 19 animals contributing both PAR and MFP and 3 animals contributing only MFP) were normalized for dye and array effects (i.e., Lowess normalization and array centering) and used for statistical analysis.
The raw signal intensity data was filtered for background and technical outliers, normalized for dye and array effects using the loess normalization procedure implemented by the Bioconductor package affy, and compared for differential gene expression using hypothesis testing statistics similar to those previously described.
For differential analysis, a linear model was fitted with age, case-control status (preterm or term), and predictive factors correcting for sex and array effects.
Fold-changes and probability values for each cDNA clone were calculated using a linear model in which the normalized intensity values in the Cy3 and Cy5 channels were adjusted for dye and array effects [ 47].
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