Clariom™ S Pico Assay, human
Clariom™ S Pico Assay, human
Applied Biosystems™

Clariom™ S Pico Assay, human

Obtain a gene-level view of the human transcriptome with Clariom S Pico Assays for human samples. Clariom S Pico AssaysRead more
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Catalog NumberNumber of Arrays
90292812 Arrays
90292930 Arrays
Catalog number 902928
Price (USD)
2,585.00
Each
Add to cart
Number of Arrays:
12 Arrays
Price (USD)
2,585.00
Each
Add to cart
Obtain a gene-level view of the human transcriptome with Clariom S Pico Assays for human samples. Clariom S Pico Assays serve as a next generation transcriptome-wide gene-level expression profiling tool, which allows for the fastest, simplest, and most scalable path to generating the results you need for your research. Based on industry-leading microarray technology, the novel Human Clariom S Assay design provides extensive coverage of all known well-annotated genes, compatibility with research sample types, scalable formats, and flexible data analysis software. Clariom S Pico Assays are the tools of choice to find expression biomarkers with known function as quickly, easily, and cost-effectively as possible.

Find answers, move on
Although the number of known transcribed genes has expanded rapidly in recent years, knowledge of the function of each gene is still evolving. Many genes and transcripts found in databases are poorly annotated or unannotated, which can complicate and prolong data analysis and interpretation. Human Clariom S Pico Assays focus on well-annotated genes, providing researchers with the ability to perform gene-level expression profiling studies and to quickly assess changes in key genes and pathways. With less time required for data analysis, Clariom S Pico Assays for human help researchers reach conclusions more rapidly.

Simple, swift biomarker discovery
• Accurately measure gene-level expression from >20,000 well-annotated genes to get to answers quickly.
• Choose a format that suits your throughput needs, processing from 1 to 192 samples a day.
• Go from data to insight in minutes with intuitive, highly visual, free analysis software designed for the biologist.

When you have precious samples, get it right the first time
• Generate robust expression profiles from as little as 100 pg of total RNA–as few as 10 cells.
• Utilize RNA from various sample types including blood, cells, and fresh/fresh-frozen or FFPE tissues.
• Preserve sample integrity and reduce data variability with an assay that does not require a globin or rRNA removal step.
• Save time and money with fully automated sample preparation options.

Clariom S solutions are available formats for single-sample (cartridge array) processing on the GeneChip™ 3000 instrument system and high-throughput automated processing (plate array) on the GeneTitan™ Microarray System, offering the flexibility to accommodate both small and large cohort studies. The complete solution comes with reagents and fast, simple Transcriptome Analysis Console (TAC) software to analyze and visualize global expression patterns of genes, pathways, and network interactions in minutes.

Get the truest level of gene-level expression
To generate robust gene-level expression, Human Clariom S Assays detect only the exons present in all known transcript isoforms expressed from a single gene locus-constitutive exons. This differs from other gene-level array technologies and shallow RNA-Seq, which provide either a biased view of gene expression or data that are complicated by variation in expression of transcript variants. By detecting only constitutive exons throughout the length of each known gene, Human Clariom S Assays generate the most accurate and truest measurement of gene-level expression available today.

Keeping biomarker identification across the transcriptome simple and swift, Clariom S Pico Assays for human provide you with the coverage you require, the reproducibility you need, and the insights you want to act on your discoveries.
For Research Use Only. Not for use in diagnostic procedures.
Specifications
ArrayTranscriptome Profiling
FormatArray Cartridge
Number of Arrays12 Arrays
Product TypeS Pico Assay
SpeciesHuman
Unit SizeEach

Frequently asked questions (FAQs)

What reagent kit should I use with my array?

Please refer to the Microarray Reagent Guide for Arrays and Expression Kits to match the correct reagents your array.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

What is an Event Score in TAC 4.0 Software?

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.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

What are the new software components of TAC 4.0?

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.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

If I have TAC 3.1 .TAC files (TAC analysis files), can I load these into TAC 4.0 Software or will I need to reanalyze?

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.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.

In TAC 4.0 Software, can I measure the quality of a single hybridization without the rest of the experiment?

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.

Find additional tips, troubleshooting help, and resources within our Microarray Analysis Support Center.