Lecture 3 Flashcards

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1
Q

What are features of models?

A

Never exactly like reality
As detailed as necessary
As simple as possible

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2
Q

What types of computational models exist

A

Mathematical and Statistical Models

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3
Q

What are the 4 assumptions LINE?

A

xp is a linear function
Errors are Independent
Errors are normally distributed
Errors have equal variance

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4
Q

How does RNA-seq work in steps?

A
  1. Fragment RNA
  2. Sequence Fragments
  3. Aligne back to genome
  4. Count reads mapping to gene
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5
Q

How do you prepare a library with Illumina?

A

Poly-A + RNA capture
RNA fragments primed
cDNA synthesized
3`ends adenlyated
adapter ligation
aplification of fragemnts

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6
Q

What is the Phred Score?

A

P = 10 ^(-Q/10) P = Propability base call incorrect Q = -10log(10)P

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7
Q

What is read trimming?

A

Chopping of ends of Read Quality Graphs

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8
Q

What is Read mapping?

A

Alignment of read to base gene sequence

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9
Q

What determines read count per base?

A

Expression level
Sequencing depth
Mapability
Noise

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10
Q

What determines read count per transcript?

A

Expression level
Sequencing depth
Mapability
Noise
Transcript length
Splicing

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11
Q

What are two methods for RNA enrichment?

A

poly A enrichment
purify transcripts with poly-A tail
enrich for mRNA
deplete non coding RNA

ribo-minus
remove rRNA
keep non coding tho

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12
Q

What is FPKM and how do you calculate it?

A

FPKM = n(i) / (l(i) * N) // n(i) = number of fragments for transcript i
l(i) = length of transcript i
N = 1 / 1000000 * Summe n(i)

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13
Q

What is TPM and how do you calculate it?

A

n(i2) = n(i) / l(i)
S = 1 / 1000000 * Summe n(i2)
TPM = n(i) / S

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14
Q

Whats the difference between TPM and FPKM

A

TPM is the same for each sample while FPKM may vary since it depends on the length of the fragment

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15
Q

How to determine differentially expressed genes?

A

linear model:
π‘Œ = πœ‡ + 𝛽1 * π‘₯𝑑reat +𝛽2 * π‘₯𝑏atch +𝛽3 * π‘₯𝑠ex

Y = read counts in a given sample
Β΅ = average read counts
xtreat = treatment condition (e.g. knock-out or drug treatment)
xbatch = batch membership (account for batch effects)
xsex = sex of the animal (account for sex effects)

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16
Q

How would 𝛽1 in the linear mopdel change if the treatment was succesfull?

A

large and different from zero

17
Q

Why are Generalzed Linear Models better ?

A

𝐾𝑖j = 𝑓( Summer X(jr) beta(ir))
gene i
sample j
factor r

It does not Follow Gaussian distribution
Variance does not depend on the mean

18
Q

How is a generalized Linear Model made up?

A

lineqar predictor
link function
varaince function with constant dispersion parameter

19
Q

What is overdispersion?

A

Variance is larger than the mean

20
Q

How does DeSeq2 work in 4 steps?

A
  1. Normalization
  2. Variance estimation
  3. Fold changes
  4. Significance
21
Q

Why do you normalize?

A

Different number of reads
Contamination
Variation in machine

22
Q

Why should you not correct very large values via shrinkage estimation?

A

To avoid false positives