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View additional product information for Clariom™ S Pico Assay, human - FAQs (902928, 902929)
27 product FAQs found
Please refer to the Microarray Reagent Guide for Arrays and Expression Kits to match the correct reagents your array.
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TAC 4.0 includes two algorithms for identifying alternative splicing events: the TAC 2.0 algorithm and the new EventPointer. Algorithmic determination of alternate splicing remains a challenging problem. TAC 4.0 supports two different approaches that have different sets of strengths and weaknesses. After considerable testing, the new TAC 4.0 “'Event Score” leverages both previous TAC 2.0 event estimation score and Event Pointer p-value and sorts the most likely alternative splicing events to the top. Of course, the TAC 2.0 event score and EventPointer p-values remain individually available.
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LIMMA: LIMMA stands for Linear Models for MicroArray data. It is an R/Bioconductor software package that provides an integrated solution for analyzing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, LIMMA has been a popular choice for gene discovery through differential expression analyses of microarray data. There are ˜8000 citations using LIMMA and Affymetrix arrays. The TAC 4.0 interface exposes the core differential expression analysis functionality including real covariates and random factors. In addition, the interface simplifies the creation of the design and contrast matrices that specify the experimental design and comparisons for the analysis.
Batch Effect Adjustment: Batch effects are systematic changes in microarray sample intensities that reflect changes in the assay sometimes found in different batches. These effects occur more commonly in larger studies in which all of the samples cannot be processed at the same time. TAC 4.0 enables the interface to the ComBat batch adjustment algorithm, which can remove the batch effects from the signals.
EventPointer: EventPointer is a Bioconductor package that identifies alternative splicing events in microarray data. TAC 4.0 incorporates an interface to this package.
Exploratory Grouping Analysis: Exploratory Grouping Analysis (EGA) is an interface to a set of R packages that offer the ability to examine the relationships between multiple microarray samples. While the scientist typically has a preconceived idea regarding the classification of the samples in an experiment, the resulting data often show additional substructure due to unexpected biological differences or batch effects. The EGA interface enables the identification of this substructure. Biological differences can be further explored using LIMMA differential expression analysis. Batch effects can be removed using ComBat to prevent them from obscuring the biology of interest.
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TAC 3.1 .TAC files cannot be opened in TAC 4.0 Software. Studies will need to be reprocessed in TAC 4.0. The new analysis can be run from .CEL files or .CHP files.
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We do not recommend this. In large-scale expression experiments using similar sample types, researchers are likely to develop their own single-array guidelines on what metric values are predictive of high- or poor-quality samples. However, these guidelines are likely to be dependent on sample type and we are unable to recommend such guidelines for all possible situations. Note that the trend toward favoring model-based signal estimation algorithms (for all microarray experiments even beyond the Thermo Fisher platform) makes single-array quality determination very difficult due to the necessity of simultaneously analyzing multiple arrays to calculate signal estimates.
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If a sample appears “on the line”, it is best to leave the sample in the experiment for analysis and simply flag it as questionable. During the initial analysis, treat all samples uniformly. Once candidate genes have been identified, review how the questionable sample changes data relative to the other replicates within the sample. It is always possible to remove a sample later in the analysis workflows.
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Pos_vs_Neg_AUC is a good first-pass metric. A value below 0.7 is an indicator that sample problems may exist. However, a value greater than 0.7 does not guarantee that the sample is good.
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Users will benefit from using the SST-RMA when comparing data with those processed using the RMA summarization only. RMA is the suggested analysis pipeline to use when doing QC on FFPE or degraded samples. For downstream analysis of FFPE or degraded samples, SST-RMA is the suggested analysis pipeline to use.
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When filtering by fold-change cutoffs, expect a larger number of differentially expressed genes compared to RMA. When comparing the number of differentially expressed genes (defined by fold-change filters) to other methods, such as RT-PCR and RNA-Seq, expect this number to align more with these methods when using SST-RMA. There is no impact on sensitivity or specificity of data. SST-RMA was designed to address comparability to other technologies. The same experimental design recommendations still apply when designing an expression study with microarrays.
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Historically, microarrays were perceived to underestimate fold-change values when compared to other methods such as RT-PCR. For customers who are filtering using fold-change cutoff, SST-RMA addresses this “fold-change compression” by applying a GC correction and also by transforming the microarray data signal to a similar signal space of other methods in a pre-processing step prior to RMA. No changes have been made to RMA. SST-RMA is the default algorithm for Human Transcriptome Arrays and Clariom D Human, Mouse, and Rat assays.
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Library files are on each array type product page. Alternatively, library files can be installed directly through TAC 4.0 by going to the 'Preferences' tab and clicking the 'Download Library Files' button located at the top left (NetAffx account required).
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Due to the amount of memory that TAC needs to operate, we STRONGLY recommend that you DO NOT install TAC 4.0 on production AGCC computers being used for scanning and operating fluidics systems.
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Both .CEL and .CHP files are accepted in TAC 4.0 Software. The exception will be previous *.exon*. CHP files and *.alt-splice*. CHP files. These two types of CHP files are no longer supported in TAC 4.0. .ARR files are recommended as they contain sample attribute information. The .CHP file is generated by TAC 4.0 in the normalization and summarization process.
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TAC 4.0 is a new software that combines many of the features of Expression Console 1.4 with the biological analysis capabilities of TAC into one workflow. Some of the new features include:
- Perform QC and statistical analysis in one software
- Ability to analyze 1000 samples
- Integration of LIMMA package (LInear Modeling for MicroArrays package) into TAC 4.0 to analyze complex data
- Perform batch effect adjustment (new)
- Perform repeated measure analysis (improved in TAC 4.0)
- Perform random factor analysis (new)
- Perform real covariate analysis (new)
- Ability to perform Exploratory Grouping Analysis (EGA)
- Integration of Event Pointer algorithm for Alternative Splicing Analysis
- Improved visualization with Splicing Viewer
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Expression array CEL files are processed and analyzed in TAC 4.0 Software. This is a free download from the Thermo Fisher website for processing CEL files.
Here are the minimum requirements:
Microsoft Windows 7 and 10 (64 bit) Professional
CPU: 2.83 GHz
Memory: 8 GB RAM
Hard drive: 150 GB free hard disk space
Web browser: Internet Explorer 11 or greater and Microsoft Edge
Here is the recommended hardware:
Microsoft Windows 7 and 10 (64 bit) Professional
CPU: Intel Pentium 4X 2.83 GHz processor (Quad Core)
Memory: 16 GB RAM
Hard drive: 150 GB free hard disk space
Web browser: Internet Explorer 11 or greater and Microsoft Edge
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No, the Clariom S array is not capable of exon-level analysis. The probes have selected to be the most constitutive probes, representing the most common areas of a gene model. This means that most of the exons in the gene model will likely not have representation on the array, meaning that it is not practical to enable exon-level analysis.
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Clariom D Assay - scan time is about 33 min.
- DAT file size about 790-800MB
- CEL file size about 68 MB
The Clariom S - scan time is about 5 min.
- DAT file size about 35-40MB
- CEL file size about 3 MB
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No, globin reduction is not necessary for the Clariom S assay. The Clariom S assay is designed to preserve sample integrity and reduce data variability without requiring a globin or rRNA removal step. This means that you can perform the Clariom S assay without the need to perform globin reduction on your samples.
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The Clariom S array is a 400 format, the correct script is FS450_0007.
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Analysis library files and annotation files can be downloaded from within the TAC Software. Please refer to the respective user manuals for detailed instructions.
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Probe cell intensity data (CEL) from human, mouse, or rat transcriptome assays are analyzed in Transcriptome Analysis Console(TAC) Software. The application uses a version of RMA analysis to create CHP files. Transcriptome Analysis Console Software can be downloaded at no charge from the website. Further statistical analysis may be performed in Affymetrix' Transcriptome Analysis Console (TAC) Software to obtain a list of differentially expressed genes and alternative splicing events. TAC Software also provides the visualization of genes, exons, junctions, transcript isoforms, pathways, and mRNA-miRNA regulatory networks.
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To install library files for the human, mouse, or rat transcriptome arrays for AGCC, simply run the executable library file installer.
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Human, mouse, or rat transcriptome arrays are 49-format.
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FS450_0001 should be used when processing human, mouse, or rat transcriptome assays. Up-to-date fluidics scripts can be obtained from the website.
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Human, mouse, or rat transcriptome assays utilize an updated installer package that is not compatible with GCOS. Human, mouse, or rat transcriptome assays require AGCC for fluidics and scanning. The Expression Console Software is required to perform QC analysis of the human, mouse, or rat transcriptome array data. Expression Console Software can be downloaded at no charge from the website.
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Affymetrix human, mouse, and rat transcriptome assays are available in three formats based on sample type and amount of input RNA:
Human, mouse, and rat transcriptome Pico assays compatible with 100-2,000 pg of RNA isolated from fresh, frozen, blood, or FFPE samples
Human, mouse, and rat transcriptome FFPE assays compatible with 20-200 ng of RNA isolated from FFPE samples
Human, mouse, and rat transcriptome assays compatible with 50-500 ng of RNA isolated from fresh, frozen, or blood samples
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You should re-analyze your data if comparing with RT-PCR or RNA-Seq data using fold change values for filtering.
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