Agilent scanner g2565ba
The Agilent Scanner G2565BA is a microarray scanner designed for high-sensitivity, high-resolution scanning of gene expression and genomic DNA microarrays. It features a dual-laser system, autofocus capabilities, and scan resolutions up to 10 microns per pixel. The scanner is capable of reading a wide range of microarray slide formats.
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7 protocols using agilent scanner g2565ba
Differential Gene Expression Analysis
Microarray Data Analysis Workflow
Data preprocessing and statistical analysis were performed by LIMMA (LInear Model of Microarray Analysis) package [19] . The quality control of raw data was carried out according to MAQC (MicroArray Quality Control) project guidelines [20] (link). The intensity raw data were background subtracted by normexp method and normalized within-arrays by LOESS and between-arrays by scale methods.
Bayesian moderated t-statistic [21] (link) was used to perform the statistical analysis and only genes with Benjamini and Hochberg [22] adjusted-p-value <0.05 were considered as differentially expressed.
GeneCards (
Mouse miRNA Expression Profiling
Profiling Mouse Kidney microRNA Expression
Genomic DNA Extraction and Array-CGH Analysis
Microarrays were scanned using the Agilent scanner G2565BA. Images were extracted using Agilent Feature Extraction software and data were analyzed with Agilent Cytogenomics v.2.5.8.11 software.
One-color Microarray Gene Expression Protocol
Microarray data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus and are accessible via:
Microarray Analysis of IL-1β Induced Transcriptome
Data analysis was performed using the Bioconductor package Limma in the programing language “R.” Target files were created containing Agilent G2565BA microarray scanner output files. Background correction, cyclic loess normalization, and averaging of repeats was performed. A linear modeling matrix was built and fitted. Gene lists were filtered discarding those unaltered by IL-1ß in the order of a log2 fold-change of < 1 and for an adjusted P value of < 0.05. A pathway analysis functional output was obtained using Signaling Pathway Impact Analysis (SPIA) in R. All was as described in previous papers from our group.13 (link) A two-dimensional projection of the microarray expression data was generated using the non-parametric dimensionality reduction. This was achieved using the t-distributed stochastic neighbor embedding (t-SNE) algorithm in the R package Rtse. The resulting t-SNE output was plotted with R package ggplot2. The array data will be deposited in NCBI’s Gene Expression Omnibus.
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