Clariom™ D Pico Assay, human, 30 reactions - FAQs

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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The GeneChip Exon Arrays represent a new array design philosophy for the most comprehensive and informative coverage with the exon as the basic unit of expression analysis. Such an inclusive design enables discovery of new and novel splicing events not previously observed experimentally. Some of the key differences are summarized below using the human arrays as an example; refer to the “GeneChip Exon Array Design” Technical Note and the “Design and Performance of the GeneChip Human Genome U133 Plus 2.0 and Human Genome U133A 2.0 Array” Technical Note for more details.

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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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