analysis of gene expression Flashcards

1
Q

focus on mRNA

A
  • easier to purify and measure than proteins
  • easier to make high throughput
  • protein expression controlled by PTMs
    • activity not proportional to mRNA expression
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

experimental considerations

A
  • uniform growth conditions
    • comparable setup without confounding variables
  • homogeneous cell population
    • heterogeneity produces misleading data
  • control choice
    • must be relevant
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

microarrays

process of image to numbers

A
  • scan
  • correct background noise
  • data transformation
  • normalisation
  • data analysis
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

microarray types

A
  • single channel:
    • 2 samples analysed separately
    • 2 sets of expression values
  • 2 channel:
    • hybridise labelled cDNA from 2 samples to same microarray
    • extratc values
    • different colour tags
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

process of microarrays

A
  • isolate and reverse transcribe mRNA
  • label cDNA with labelled nucleotides
  • hybridise
  • scan and analyse
  • calculat eintensity relative to normal background
    • some chips have inherently higher intensity than others
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

microarray data transformation

A
  • difference between or ratio of 2 samples
    • ratios for larger differences
    • depends what you are comparing
  • be wary of small numbers
    • differences more likely due to chance
  • to identify differential expression:
    • plot of log ratios → focus on tails of gaussian distribution
    • plot of log intensity → above or below diagonal line
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

microarrays

data normalisation methods

A
  • global intensity
  • houskeeping genes
  • exogenous/spiked control
  • intensity dependent linear
  • loess
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

experimental variables affecting microarray intensity levels

A
  • number of mRNA copies (want to be the only variable)
  • hybridisation efficiency
  • cross-hybridisation
  • efficiency of reverse-transcription
  • marker incorporaiton into cDNA
  • scanning efficiency
  • activity of fluorescent dyes
  • equipment differences
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

global intensity normalisation

A
  • assumption:
    • same total expression for sample and control
  • method:
    • calculate sum of intensities for all genes for sample and control
    • use ratio of intensity sum to calculate normalisation factor, k
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

housekeeping gene normalisation

A
  • assumption:
    • housekeeping gene mRNA expression is constant
      • tubulin, actin
    • their expression does not depend on cell condition/status
  • method:
    • use ratio of means of hk gene intensities to calculate k
      • m(sample)/(control)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

spiked control normalisation

A
  • add mRNA from foreign genes
    • 5-10 B. subtilis genes with artifical polyA tails
  • equal amounts of exogenous controls
    • use average intensities to compute k
    • whole normalise data sets
  • can detect large changes in genome-wide mRNA levels
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

intensity dependent linear normalisation

A
  • predicts experimental expression using control expression minus intercept for normalisation
  • assumptions:
    • constant mRNA levels
    • all genes on the microarray have the same variance
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

intensity dependent linear normalisation

method

A
  • fit data to linear regression model
  • yi = α + βxi + εi where:
    • yi = background-subtracted intensity of gene in set 1 (experimental)
    • xi = background-subtracted intensity of gene in set 2 (control)
    • α = y-intercept of microarray data
    • β = normalisation factor
  • normalise yi:
    • y’i = (yi - α)/β
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

intensity dependent linear normalisation

advantages vs disadvantages

A
  • advantages:
    • each gene contributes equally to nromalisaiton factor
      • prevents high expression bias
    • can correct skew in data with y-intercept correction
  • disadvantages:
    • linear data only
      • can you be sure of linearity
    • assumes constant mRNA levels
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

M vs A plot

A
  • detemrines whether non-linear normalisation is needed or not
  • minus vs add
  • difference in log intensity of each channel
    • average log intensity of each halved
    • plot against each other
  • quick overview
  • if no change in expression:
    • points lie straight on x=0
    • otherwise loess normalisation needed
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

loess normalisation

A
  • intensity dependent non-linear normalisation
  • local running average subtracted
  • applies different correction to different parts of data set as appropriate
  • apply loess function to MA plot
    • applies rules of logs to M and A to correct for M
  • good for skewed or non-linear data
  • more complex and difficult to calculate
17
Q

RNAseq

A
  • resolves intensity conversion problem
  • sequence counts only
  • prevents bias against non-coding RNAs/microRNAs and RNAs without probes
  • single cell resolution
  • calculate reads per nt and get average score for each gene
    • repeat with 3’ end
    • sum of reads within the gene divided by length of gene