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Feature extraction image analysis software version 10.7.3

Manufactured by Agilent Technologies

Feature Extraction Image Analysis Software (Version 10.7.3) is a software solution designed for image analysis. The core function of this software is to extract relevant features from digital images.

Automatically generated - may contain errors

2 protocols using feature extraction image analysis software version 10.7.3

1

Microarray Analysis of Mammary TAM RNA

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Total RNA was extracted from mammary TAMs or tumor cells from PyMT mice fed CDDO-Me or control chow for 8 weeks using the miRNeasy kit (Qiagen). RNA integrity and quantification were determined using a 4200 TapeStation (Agilent). Samples were amplified and labeled using an Agilent Low Input Quick Amp Labeling Kit and were hybridized against Universal Mouse Reference (Stratagene) to Agilent Whole Mouse Genome DNA microarrays (G4852A) in a common reference-based design as previously described52 (link). Agilent Feature Extraction Image Analysis Software (Version 10.7.3) was utilized for the extraction of data in raw microarray image files.
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2

Microarray Analysis of TGF-β Signaling

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RNA was isolated at various time points and processed for hybridization using an Agilent SurePrint G3 Human Gene Expression 8x60K Microarray kit (product no. G4851A) (21 (link)). Quality control was performed using an Agilent 2100 Bioanalyzer; all samples had RNA integrity values of >9. Agilent Feature Extraction image analysis software version 10.7.3 was used to extract data from raw microarray image files. Microarray data were log2 lowess–normalized and filtered for probes with intensity of ≥1.5-fold over local background in the Cy3 or Cy5 channel. Expression values were multiplied by −1 to convert to log2 (Cy3:Cy5) ratios. After probes with >20% missing data were excluded, 41,589 probes passed the filtering criteria. The probes were median-centered across all arrays. Missing values were imputed based on the k nearest neighbor algorithm (k = 10) using Euclidean distance as the distance metric. Differentially expressed genes were selected by Significance Analysis of Microarrays analysis using a 2-class unpaired t-test. Expression data for 1,143 probes differentially expressed between untreated and TGFβ-treated samples were selected at a false discovery rate of 1.1%.
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