Clariom™ D Assay(大鼠)先前被称为 GeneChip™ 大鼠转录组阵列 1.0 (RTA 1.0)了解更多信息
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货号
阵列数量
902666
30 阵列
货号 902666
价格(CNY)
-
阵列数量:
30 阵列
Clariom™ D Assay(大鼠)先前被称为 GeneChip™ 大鼠转录组阵列 1.0 (RTA 1.0)。 使用新一代的转录组水平表达谱分析工具 (Clariom D Assay),可从转录组深处加速生物标记物的发现。Clariom D 检测可详细显示转录组细节视图,采用最快路径得到可操作结果。Clariom D 分析可用于人类、小鼠和大鼠,让转录研究科学家快速而简单地得到高保真度的生物标记物签名。基于业界领先的微阵列技术,新型 Clariom D 分析设计提供较复杂的转录组范围内的基因和外显子水平的表达谱,包括在三天的实验中检测编码和长非编码 (lnc) RNA 的选择性剪接事件的能力。
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.
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.
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.
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.