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  • Cufflinks statistical model

    I needed a little help in understanding the abundance estimation by
    Cufflinks.
    Please refer to cufflinks supplementary methods.

    Let me reiterate some of the key points/definitions; for the sake of
    convenience of explanation.

    ρ(t) = abundance of transcript t
    α(t) = probability of choosing a transcript t
    [identified by abundance and length]
    β(g) = sum(α(t)) (t belongs to g) = probability of choosing a transcript from
    a locus g
    γ(t) = probability that chosen transcript has given abundance and length

    Question 1: Does that mean that a transcript is fully identified by its
    length and abundance ?

    Question 2: In the parameter estimation section, I didnt quite understand
    how MLE of β becomes X(g)/M.
    Shouldnt it be the solution of ∑ ∂(X(g).log(β(g)))/∂β(g) = 0 ?

    Question 3: I dont understand importance sampling method much, but is
    there an intuitive way of understanding how is γ estimated from input
    variable i.e. reads ?

    FPKM calculation has l(t) in denominator. Cufflinks should accept any
    SAM/BAM file regardless of whether its passed through Tophat. If I pass to
    cufflinks, the reads aligned to transcriptome (refseq), and I dont provide
    any annotations, then:

    Question 4: How is a locus designated ?
    Question 5: How is l(t) estimated for FPKM calculation; length of a
    transcript should be smaller than a locus?


    Finally, how can I use cufflinks without involving genome alignments !?

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