Chapter 11 + 12

Question 1

Similarities between data analysis pipelines between different omics approaches

Answer 1

-All technologies yield many measurements for each sample
-Same way of handling dimensionality
-yields hundreds or thousands of variables per sample like different genes, proteins or metabolites

Question 2

Samples organised in matrix

Answer 2

Rows: the samples
Columns: the variables (like genes)

Question 3

Four components of the generalized data analysis pipeline

Answer 3

1. Experimental design and data collection
2. Data preprocessing and quality control
3. Data analysis
4. Biological interpretation

Question 4

The experimental design can have large impact on the statistical power and therefore the …

Answer 4

Conclusions that are reached

Question 5

First step in experimental design

Answer 5

Frame a biological question

Question 6

What is the aim of the biological question?

Answer 6

Determine the hypothesis that will be tested and the statistical test that will be executed.

Question 7

What does the biological question determine which is needed for an interpretable and successful outcome?

Answer 7

The experimental preconditions

Question 8

Three types of main objectives which require a different type of experimental design

Answer 8

1. Detection of responsive features under controlled experimental conditions (perturbation study)
2. detection of biomarkers
3. identification of regulatory or mechanistic relationships between variables

Question 9

Experimental designing after biological question

Answer 9

Identify noise factors and design the experiment

Question 10

Noise factors

Answer 10

Factors that can disturb a proper measurement (from the biological experiment up to and including the measurement)

Question 11

Noise factors can lead to …

Answer 11

bias

Question 12

Three basic principles to deal with noise factors.

Answer 12

1. Replication
2. Randomization
3. Blocking

Question 13

What is the aim of the experimental design?

Answer 13

Ensure reliable measurements free from bias

Question 14

Replication

Answer 14

Duplicate, repeat or perform the same measurement more than once
> obtain an estimate of the experimental error

Question 15

On what factor is the type of error which is estimated with replication dependent?

Answer 15

On how the replication is done
> For estimating and controlling biological variability: different organisms or batches of cells samples should be processed in the same manner.

Question 16

Types of replication errors

Answer 16

-Repeatability: error based on repeats of sample measurement (same sample)
-Reproducibility: error based on sample workup or sampling/the whole experiment (larger errors)

Question 17

Types of replicates

Answer 17

-Biological replicates: error based on the whole experiment (also the organisms) > not interested in 1 individual
-Technical replicates: to gain statistical power

Question 18

Randomization

Answer 18

Requiring the experimenter to use random choices for every factor that is not of interest but might influence the outcome of the experiment
> random selection of individuals for groups
> hybridization of mRNA samples from treatment and control group: sensitive for external factors: important to not measure all controls first and then all treated: impossible to distinguish between time effect (not interesting) and treatment effect (interesting)

Question 19

Confounder

Answer 19

A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship (e.g. time: randomize over time to eliminate the bias)

Question 20

Blocking

Answer 20

Arranging experimental samples in groups (blocks) that are similar to one another
(e.g. gender, or different columns)
> but within the groups the variation of treated/control needs to be similar
> or: blocks because not all measurements can be done on one day
> eliminating confounding effect of gender or LC column

Question 21

General rule for blocking

Answer 21

Block what you can, randomize what you cannot (treated/control is not blockable)

Question 22

Which instruments show drift in time?

Answer 22

GCMS and LCMS (for metabolomics and proteomics)

Question 23

Where is the order in which the samples defined and why is it of importance?

Answer 23

In the measurement design: important because in LCMS or GCMS when the number of samples is large and several batches are needed, instrumental drift causes samples to be measured in the beginning to be slightly different than when measured at the end of the series.

Question 24

Why is randomization crucial in different batches in the LCMS or GCMS

Answer 24

Because of instrumental drift, when no randomization is performed, the observed difference could be only due instrumental drift and there is bias. the actual results are not destinguishable from the bias.

Question 25

In data preprocessing: disturbances need to be removed from data which can enter during sampling, sample workup, measurement. Which two types of disturbances do we know?

Answer 25

-Disturbances of a whole sample
> different amount of sample measured
> different dilution of samples
> sample workup unequal
> effect of order of measuring (begin/end of day)
-Disturbances of a single variable within a sample (e.g. singel metabolite)

Question 26

Which methods of preprocessing are used for correction of whole sample disturbances?

Answer 26

Normalization methods

Question 27

Normalization methods

Answer 27

-Internal standard
-QC samples

Question 28

Internal standard

Answer 28

Compound added to each sample in equal amount which does not occur naturally
> intensity of the standard has to be the same in all samples
> difference across samples: correction of all variables with same factor

Question 29

Quality control samples

Answer 29

For correction of instrumental drift
> pooled samples are used
> after each 8 samples the QC sample is measured
> many QC sample measurements over whole day
> intensity of each metabolite should be the same but due to instrumental drift it may not be the same at different time points
> use differences to correct studied samples inbetween the QC samples

Question 30

Normalization

Answer 30

Correct for different dilutions e.g. urine is less diluted in the morning: correction by certain ‘concentration measure’ > not true concentrations anymore but samples are better comparable.

Question 31

Which correction methods are used for single variable disturbances due to column aging in LCMS/GCMS?

Answer 31

Alignment methods for aligning peaks at different retention times for different samples such it is clear they belong to the same variable (metabolite/protein)

Question 32

Correction methods for baseline (background signal) is unequal to zero?

Answer 32

Background correction methods

Question 33

How is clean data stored after data preprocessing?

Answer 33

data matrix
> for metabolomics data, after preprocessing and normalization: normalized data matrix: starting point for data analysis and biological interpretation

Question 34

What can a zero mean in the data matrix?

Answer 34

Not present, or below detection limit.

Question 35

Are different variables always measured in the same units?

Answer 35

No, but often yes

Question 36

11.4 data analysis