Sentence examples for background correction using a from inspiring English sources

Fluorescence intensities of cells encircled by ROIs were calculated by averaging corresponding pixel intensities in the original image, excluding pixels within encircled nuclei, after background correction using a blank (no cell) region of the image.

Pre-processing of the array data included background correction using a normal-exponential convolution model [ 82] and quantile normalisation [ 83].

After background correction using a Normal plus Exponential convolution model, which adjusts the foreground to the background, we used an offset to dampen the variation of the log-ratios for intensities close to zero.

Additionally, the neqc method from the limma package has been included, which performs a background correction using a normal-exponential-modeling approach [ 12] followed by a quantile normalization of all regular and control probes together, and a log2-transformation on the dataset.

GCRMA background correction used an affinity measure model based on probe sequences and mismatch intensities.

In order to examine the assumption of normally distributed background noise, we performed background correction using the convolution model and plotted the estimated background intensities versus a normal distribution using a quantile-quantile plot for three spike-in data sets.

Images were analyzed after background correction using the rolling ball radius tool (50 pixels).

The expression ratios were normalized without background correction using the print tip loess method [31] of the limma package [32] from Bioconductor.

GCRMA 19, 20 was used for normalization and background correction using R statistical language.

Raw PM intensities from each array were subjected to local background correction using MAS5 method, log2-transformed and median-centered.

Following background correction using the median foreground minus the median background signal intensity for each channel, the log 2 ratios were calculated for each spot.