The cDNA of interest in a microarray experiment is known as this
The ssDNA on the microarray is known as this
Fluorescently labelled cDNA that shows as red on a microarray is labelled with this
Fluorescently labelled cDNA that shows as green on a microarray is labelled with this
Fluorescently labelled cDNA with the same gene expression on a microarray appears this colour
Fluorescently labelled cDNA labelled with Cys5 on a microarray appears this colour
Fluorescently labelled cDNA labelled with Cys3 on a microarray appears this colour
The most important phase of a microarray experiment
What happens after a microarray experiment has been carried out
What happens after image analysis of a microarray experiment has been carried out
If image analysis fails quality measurement, the experiment returns to this phase
If image analysis passes quality measurement, the experiment proceeds to this phase
The results of a microarray analysis after normalisation are in this format
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
The first step in image analysis, that determines the location of spot centres
The second step in image analysis, that classifies pixels as spot or background
The third step in image analysis, that measures intensity of each spot and each dye
ratio vs intensity
An MA-plot compares...
An MA-plot that is closer to zero indicates...
Two basic statistical tools that are useful in estimation of microarray data
The data analysis approach best suited to answer the question "which genes are different in these two samples?"
The data analysis approach best suited to answer the question "are there gene groups in these samples?"
The data analysis approach best suited to answer the question "which group does this sample belong in?"
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
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...
In K-means, you make every point find which ____ it is closest to
Clustering is useful for...
Clustering always requires confirming with...
In discrimination, you begin with data with known classes, called a...
In discrimination, you apply this to a learning set
In discrimination, the application of a classification technique to a learning set generates this
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