Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire genome - in a single experiment. Such experiments can generate very large amounts of data, allowing researchers to assess the overall state of a cell or organism. Data in such large quantities is difficult - if not impossible - to analyze without the help of computer programs.
Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software. It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project was created to identify a set of standard strategies. Companies exist that use the MAQC protocols to perform a complete analysis.
Most microarray manufacturers, such as Affymetrix and Agilent, provide commercial data analysis software alongside their microarray products. There are also open source options that utilize a variety of methods for analyzing microarray data.
Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. Dye normalization for two color arrays is often achieved by local regression. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots. A common method for evaluating how well normalized an array is, is to plot an MA plot of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.
Raw Affy data contains about twenty probes for the same RNA target.
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This course will take place from 3rd to 7th June 2024.It will introduce the workflows and techniques that are used for the analysis of bulk and single cell RNA-seq data. It will empower students to
Bioconductor is a free, open source and open development software project for the analysis and comprehension of genomic data generated by wet lab experiments in molecular biology. Bioconductor is based primarily on the statistical R programming language, but does contain contributions in other programming languages. It has two releases each year that follow the semiannual releases of R. At any one time there is a release version, which corresponds to the released version of R, and a development version, which corresponds to the development version of R.
Transcriptomics technologies are the techniques used to study an organism's transcriptome, the sum of all of its RNA transcripts. The information content of an organism is recorded in the DNA of its genome and expressed through transcription. Here, mRNA serves as a transient intermediary molecule in the information network, whilst non-coding RNAs perform additional diverse functions. A transcriptome captures a snapshot in time of the total transcripts present in a cell.
Explores quantile normalization in genomics, emphasizing data preparation, loading, filtering, and the significance of accurate gene expression analysis.
Emerging evidence suggest that transcription factors play multiple roles in the development of pancreatitis, a necroinflammatory condition lacking specific therapy. Estrogen-related receptor γ (ERRγ), a pleiotropic transcription factor, has been reported t ...
Disruption of brain development early in life may underlie the neurobiology behind schizophre-nia. We have reported more immature synaptic spines in the frontal cortex (FC) of adult Roman High-Avoidance (RHA-I) rats, a behavioural model displaying schizoph ...
2023
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Differential expression analysis of single-cell transcriptomics allows scientists to dissect cell-type-specific responses to biological perturbations. Here, the authors show that many commonly used methods are biased and can produce false discoveries. Diff ...