V7

Question 1

the principles of microarrays (general info being it)

Answer 1

spotted microarray: can measure the expression level of more than 20.000 genes in a single experiments

hybridisation by forming H-binds between complementary nucleotide base parts -> DNA spots attached to solid surface

• samples are labeled with fluorescent dyes

Question 2

how to set up a microarray plate

Answer 2

column are the different samples , every column is given the same sample all the way

• every row is tested for a different gene
• gene expression levels are then seen on plate

Question 3

common microarray workflow

Answer 3

1. create oligo-arrays
2. acquire samples, extract RNA
3. RNa to DNA reverse transcription
4. PCR(optional), Cy3 and Cy5 labelling
5. hybridisation and scanning
6. data storage
7. extract expression levels
8. data normalisation
9. gene expression analysis
10. data interpretation

Question 4

why normalise ?

Answer 4

• remove technical variation from noisy data
• assumption: global changes across samples are due to unwanted technical variability
• to remove these differences has the potential to remove interesting biologically driven variation

Question 5

different options for normalisation/standardization

Answer 5

mean centering: new = old - mean(of group)
standard screw/student’s T-statistic: new = (old - mean) / SD
quantile normalisation: rank based method

Question 6

quantile normalization

Answer 6

• normalises between the samples -> makes them very homogenous, even with samples from different tissues
• > might remote differences between the samples that naturally occur

Question 7

ratios

Answer 7

• simplest way to look at differences
• ratio = mean(tissue1)/mean(tissue2) -> the ratio is very bias, not advisable
• log2 ratio = long2(mean(tissue1)/mean(tissue2)) - so the one ‘tissue’ doesn’t overtake the other - its more unbias

Question 8

what defines statistical significance

Answer 8

• if it has been predicted as unlikely to have occurred by chance alone
• measured by probability value (p-value)
• rejected if p < 0.5
• the smaller the p value, the larger the significance

Question 9

student’s t-test

Answer 9

• compare the difference between two groups
• assumes normal distribution of data
• t.test function in r

single sided t-test

• hypothesis groupa < group b
• > is more powerful

two sided t-test

• hypothesis groupa != groups
• > always this unless you have a certain hypothesis/previous info

Question 10

one sample thest

Answer 10

• test the hypothesis(H0) that the population mean is qual to a specific value µ0

Question 11

two sample t-tests

Answer 11

• independents (unpaired) samples
• > 100 people - 50 control, 50 treatment
• > preferably two groups in equal size and variance

paired sample (“intervention” in the middle)

• > generally preferred
• > “repeated measures” t-test
• > before and after treatment measurements
• > reduces (or eliminates) the effects of confounding factors

Question 12

normality assumptions - parametric tests

Answer 12

• assume the input data follows a known distribution
• each of the two populations being compared should follow a normal distribution
• variance of the two populations are also assumed to be equal
• samples should be random and independent

Question 13

how to test for normality

Answer 13

shapiro-Wilk test : shapiro.test()

Question 14

how to behave for variance between populations

Answer 14

if not : Welch’s t-test (default)

if yes: use t.test(,var.equal = TRUE)

Question 15

normality assumptions - non-parametric tests

Answer 15

• does not assume data follows a certain distribution
• > often rely on rank methods

Tests:

Mann_whitney U test
wilcox. test
Kruskal Wallis test
friedman test

Question 16

Mann-Whitney U test

Answer 16

2 -group Mann-Whitney U test
- wilcox.test(y~A) : y = numeric measurements A= boolean factor (GroupA/GroupB)
-wolcox.test(x,y): x=numeric group A measurements y=numeric group B measurements
2-group wilcoxon signed rank test
- wilcox.test(x,y,paired=TRUE) : where x and y are numeric “repeated measurements”

Question 17

Kruskal Wallis test

Answer 17

• one way anova by ranks
  kruskal. test(y~A): y is numeric, A is a factor (many levels)

H0 = are all groups from the same population?

Question 18

friedman test

Answer 18

• randomised block design
  friedman. test(y~A|B): y numeric data values, a is a grouping factor, b is a blocking factor
• potato yield (y) of types of potato plants (A), which have been measured across different fields (B)

Question 19

Corellation

Answer 19

• is a measure of dependence between two variables
• R provides the cor()
• corellations are useful because they can indicate a predictive relationship that can be exploited in practice

correlation != causation

Question 20

types of corellation

Answer 20

pearson - no transformation : fast, but sensitive to outliers
spearman - rank based transformation: slower, but more robust

Question 21

multiple testing and what is accepted as significant

Answer 21

• we test gene expression data for significant difference : does gene a significantly differ between conditions?
• perform many of these tests (commonly 20.000 genes)
• as the number of comparisons increases, it becomes more likely that the groups being compared will appear to differ in merman of at least one attribute
• to preserve the 1 in 20 threshold compensation for the amount of tests we performed is needed
• simples correction (bonferroni)
  p-value <0.05/#samples -> value < 2.5x10^-6

Question 22

different errors in multiple testing

Answer 22

type 1- error : calling a gene significantly changed, even if its just by chance - avoid by bonferroni correction

type 2 error: missing a significantly changed gene - avoid by benjamin i-hochberg false discovery rate procedure

Question 23

how to adjust the p-value

Answer 23

using the p.adjust function

p.adjust(0.0015, “bonferroni”, 10)
- p wert = 0.0015, # tests = 10
adjusted p-values below < 0.05 are considered sigificant

Question 24

how to get free microarray data

Answer 24

• gene expressions Omnibus(NCBI)
• > only storage and retrieval
• array Express (EBI)
• > has gene expression atlas, curated, re-annotaded archive data
• > A storage, retrieval and analysis
• > different biological conditions across experiments