Your English writing platform
Discover LudwigSuggestions(1)
Exact(1)
Though in this example we focused on a single aspect of the microarray channel intensity distributions (i.e. ADO rate), in our algorithm we make use of the entire distribution.
Similar(59)
Log-ratios of the bound (Cy5) and input (Cy3) microarray channel intensities were computed for each microarray.
Normalization of the final set of microarrays proceeded by computing log ratios of the bound (Cy5) and input (Cy3) microarray channel intensities for each microarray and then microarrays were normalized to one another using quantile normalization under the assumption that all samples have identical overall methylation levels.
With such issues in mind, we implemented a chromosome copy number classification algorithm in MATLAB (MathWorks, Natick, MA, USA) that makes use of parental genotypes and the observed distribution of unprocessed single-cell microarray channel intensities grouped by parental context (Rabinowitz et al., 2007, 2008; Johnson et al., 2008).
Data were of two general types, dual channel ratio data corresponding to spotted cDNA microarrays and single channel intensity data corresponding to Affymetrix microarrays.
For each microarray, the mean of those standard deviations was calculated with the data from spots with a background-corrected reference channel intensity less than 150 filtered out.
Log2 transformed paired channel intensity values (background subtracted and normalized) from replicate hybridizations were subjected to Significance Analysis of Microarrays (SAM) software [ 51] to identify statistically significant up- or down-regulated genes.
Individual channel intensity values ranged from 1-16,383.
Specific signal intensity for SOD2 staining was calculated by normalizing the Cy3 channel intensity to DAPI intensity.
Individual microarrays were processed as single channel intensities (Cy5, Cyanine 5; and Cy3, Cyanine 3) since, for these studies, the two channels contained unique biological samples [ 18] and the search for a binary classifier was more straightforward.
The Agilent ratio data was produced in two different ways: the Agilent ratio data named without "Z" as the last letter was from a non-self microarray, with the log ratio derived from the red channel intensities divided by the green channel intensities; the Agilent ratio data with "Z" as the last letter was from self-self microarrays, with the log ratio obtained from two different microarrays.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com