L11 - Microarrays
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37 terms
Terms | Definitions |
|---|---|
 target | The cDNA of interest in a microarray experiment is known as this |
| probe | The ssDNA on the microarray is known as this |
| Cys5 | Fluorescently labelled cDNA that shows as red on a microarray is labelled with this |
| Cys3 | Fluorescently labelled cDNA that shows as green on a microarray is labelled with this |
| yellow | Fluorescently labelled cDNA with the same gene expression on a microarray appears this colour |
| red | Fluorescently labelled cDNA labelled with Cys5 on a microarray appears this colour |
| green | Fluorescently labelled cDNA labelled with Cys3 on a microarray appears this colour |
| experimental design | The most important phase of a microarray experiment |
| image analysis | What happens after a microarray experiment has been carried out |
| quality measurement | What happens after image analysis of a microarray experiment has been carried out |
| microarray experiment | If image analysis fails quality measurement, the experiment returns to this phase |
| normalisation | If image analysis passes quality measurement, the experiment proceeds to this phase |
| matrix | The results of a microarray analysis after normalisation are in this format |
| analysis | What happens after normalisation has been carried out |
| estimation, testing, clustering, discrimination | The four main types of analysis that can be performed on microarray results |
| addressing | The first step in image analysis, that determines the location of spot centres |
| segmentation | The second step in image analysis, that classifies pixels as spot or background |
| information extraction | The third step in image analysis, that measures intensity of each spot and each dye |
| ratio vs intensity | An MA-plot compares... |
| better normalisation | An MA-plot that is closer to zero indicates... |
| histogram, boxplot | Two basic statistical tools that are useful in estimation of microarray data |
| linear model | The data analysis approach best suited to answer the question "which genes are different in these two samples?" |
| clustering | The data analysis approach best suited to answer the question "are there gene groups in these samples?" |
| discrimination | The data analysis approach best suited to answer the question "which group does this sample belong in?" |
| GxN matrix | Combining arrays of data on G genes for N arrays will give this result |
| not enough samples, assumes normal distribution | Two issues with using a standard T-test to compare microarray samples |
| permutation test | A two-sample comparison test that uses the data to generate the null value |
| hierarchical clustering, K-means | Two methods of performing clustering on microarray data |
| its own cluster | In hierarchical clustering, you make every point... |
| K-cluster centre | In K-means, you make every point find which ____ it is closest to |
| exploratory data-mining | Clustering is useful for... |
| further experiments | Clustering always requires confirming with... |
| learning set | In discrimination, you begin with data with known classes, called a... |
| classification technique | In discrimination, you apply this to a learning set |
| classification rule | In discrimination, the application of a classification technique to a learning set generates this |
| class assignment | In discrimination, the application of a classification rule to a set with unknown classes results in... |
| k nearest neighbour, classification tree | Two types of classification techniques used in discrimination |
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