Layman85

@Greippi and CharonY: Thank you very much for your fast and detailed answers! It helped much!

I still have some questions about the usage of oligonucleotide- and cDNA-Microarrays:

Is it possible to conduct supervised classification using cDNA-microarrays, or can one perform that task only with oligonucleotide microarrays?

As I understood it, with cDNA-microarrays there aren't any target variables, since the DNA that is placed on the cDNA-chips is a mixture of the colored DNA of the probes of the two classes. Does that mean, one can use them only for clustering genes that are similiar in their value of expression and direction towards one of the classes?

In contrast an oligonucleotide microarray only investigates the expression of genes from one probe. So in this setting every array represents one patient, that is belongs to one class of the two. Is that true?

Greetings

Roman

Hello!

I am currently attending a lecture called "Statistical Methods in genomics and proteomics"

in the course of my master's course in Statistics.

Being a real layman in genetics, I have several problems in understanding. I am sorry about

the amount of the questions, but asking them the professor would be a bit much I guess.

Probably some of the questions will depend on each other, so, I guess, not each

has to be answered individually. You may also give rather general answers!

I would be very thankful, if some people give answers in a way that is conceivable to a novice!

Having already searched the internet, I wasn't able to find answers that were suitable for a novice.

The questions sound most probably very silly to a person, who is familiar with the subject.

Exuse my english!

I'll just list them up in the following:

1. How can one isolate a gene, that is determine the part of the DNA that is responsible for

"one RNA"?

2. This is about microarrays: In the lecture notes I have, it says, that one can determine

the amount of the mRNA, that is taken up by the different genes. Does that mean, that not the

whole gene is combined with the mRNA, because if the whole gene is attached by mRNA one would know

the amount of the mRNA, because one knows the length of the gene?

3. About the Dyeswap-method: I don't understand the whole concept.

Why are the colours swapped between the comparison group and the control group?

In the lectore notes, it says, that first of all the mRNA is extracted from the object of

interest: What is the object of interest in general? Is it the gene, that is to be studied?

The next step is: transcribing the mRNA into cDNA: What does that mean and why

does one do that?

Below the headline "Measure model for cDNA-Microarrays" it says, that the gene expression

is measured under two different conditions. What are those conditions?

How do the two colors emerge? I mean, how is the gene activity turned into color levels?

One measure is defined as a function of the intensity of the color under condition "A" minus the

the same function of the intensity under condition "B". The measure can be additively decomposed

into: the true fold-change of the gene activity under condition "A" in comparision to that under

condition "B", the effect of the color and measurement error. I thought, that the color would

MEASURE the fold-change, so why is the effect of the color independent of the true fold-change?

4. The intensity of the color ist measured using image recognition:

The relevant information in every informative area consists of: intensity of the foreground,

intensity of the background and quality of the information. What is actually meant by "quality

of the information"?

The intensity of the color of the background is determined rather than that of the foreground,

despite the latter being the ultimatily interesting component. Why is that?

Sometimes a correction of the background color is done. Why and how?

5. I don't get the idea of normalisation: Why doesn't one expect differences of the medians

accross the arrays? I thought, so that the two groups (comparison and control) can differ

in the gene activity, the medians in different arrays have to be different, since some

of the arrays represent the comparison and some the control group.

A similiar question: To attain normalisation one can transform the data to have median zero

in every array: Why does the median has to be zero? Isn't it possible (respectively the rule),

that one color is more dominant, and as a consequence M = log2® - log2(G) has a nonzero median?

6. What is the difference between cDNA Microarrays and Oligonucleotid Microarrays - other than in

the first there are spaces between the spots and in the latter not?

With oligonucleotid Microarrays there exist Perfect Matches (PM) and Mismatches (MM): In the

lecture notes, I have, there is a diagram showing two rows, one representing the PMs and one

the MMs, one row is a probe pair. Why are the first called Perfect MATCHES? I thought, in this setting

a "match" would occur, when the two elements in a probe pair happen to be equal, but I don't understand,

how a single element can be called a "match" - the same with MMs. What are PMs and MMs anyway?

Thank you very much in advance!

Greetings

Roman Hornung