analysis of gene expression 2 Flashcards

1
Q

identification of differentially expressed genes

A
  • data analysis methods:
    • fold change/thresholds
    • t-test
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2
Q

fold change

A
  • average fold changes of experimental replicated
    • or average of log ratios (more accurate)
  • decide threshold
  • modification = additional criteria for intensity change
    • require absolute change e.g. by 10 units
    • or floor intensity data (all below 10 to 0)
    • reduces number of false positives and corrects for systematic errors
    • fewer hypotheses to be tested
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3
Q

fold change advantages

A
  • simple to implement and easy to calculate
  • straightforward interpretation
  • can use with few replicates
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4
Q

fold change disadvantages

A
  • small intensity changes can produce large calculated fold changes in poorly expressed genes
  • doesn’t account for noisy data
    • outliers have large effect on average fold change
  • not statistically-based
    • threshold?
    • convenient not mathematical
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5
Q

t test analysis

A
  • statistical version of fold change
  • assumes 2 samples are normally distributed
  • investigates whether their means are the same or different by calculating t statistic for each replicate
  • null hypothesis - average expression is same for both samples
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6
Q

multiple correction testing

A
  • more tests means you increase the number of apparently significant results you would expect by chance alone
  • if alpha = 0.05, you would expect 50 unusual results in 1000 tests
  • need to correct for this
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7
Q

t test

advantages and disadvantages

A
  • advantages:
    • statistical
    • fewer false positives than fold change
    • can combine RNAseq and microarray data
  • disadvantages:
    • usually few replicates - limits statistical power
    • can lead to large gene-to-gene fluctuations in calculated standard deviation with small replicate number
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8
Q

DNA binding sites

A
  • indicate how translaiton is controlled
  • can predict regions that lead to expression of particular genes
  • experimental identification or de novo prediction to create binding site library
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9
Q

using DNA motif knowledge

A
  • search sequence for known sites
  • identify and search for restriction sites
  • use information to model binding site
  • create consensus
    • decide number of allowed mismatches
    • depends on sequence properties
  • create weight matrix
  • create position frequency matrix
  • create position weight matrix
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10
Q

binding site PWM

A
  • probability of base b in position i (b,i)
  • pseudocount to correct for finite number of input sequences
  • sigma to represent general probability of base occurrence
  • score sites to indicate certainty/uncertainty of particular base at that position
  • search sequence for objects that are likely to arise from that PWM
  • score directly related to binding energy of DNA-protein interaction
    • statistical and energy-based model
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11
Q

assumptions of DNA motif knowledge approach

A
  • nucleotide at one position has no effect on nucleotide present at adjoining position
  • TFs have strict spatial requirements in binding sites that preclude variable spacing
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12
Q

de novo prediction of DNA binding sites

A
  • use of gene expression studies to identify coexpressed genes
  • something upstream of coexpressed genes may explain expression behaviour
  • statistical methods to identify the motif of interest in available sequences
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