Changeset 1065


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Timestamp:
May 14, 2009, 12:28:59 PM (12 years ago)
Author:
Jari Häkkinen
Message:

Reverting changes commit by mistake to README.

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1 edited

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  • plugins/base2/net.sf.basedb.normalizers/trunk/README

    r1064 r1065  
    5050== qQuantile normalization ==
    5151
    52 qQN geometric mean non-logged values!
    53 qQN must have positive numbers!
    54 
    55 The assays are normed against a selectable sub-set of the assays
    56 ... If a probe has no well-defined measurement (i.e., no assay in
    57 the reference has a well defined value for a probe) it is simply
    58 ignored from the target distribution.
    59 
    60 Ja, jag tankte att man far justera q till maximalt antal icke nan ...
    61 men som sagt an sa lange ar q==100.
    62 
    63 Den begränsning som finns nu är att det måste finnas lagom många
    64 väldefinierade mätvärden per assay (några hundra per assay) annars
    65 kraschar nog programmet. Jag kommer att lösa detta genom att välja
    66 antalet bins i distributionsberäkningen som #bins=max(100,N_i/10)
    67 i=1...#assays och kräva att #bins är minst 10. Kravet är alltså att
    68 varje assay måste ha minst 100 väldefinierade punkter. Om inte kravet
    69 är uppfyllt stannar programmet med ett någorlunda trevligt meddelande.
    70 
    71 In q-quantile normalization each assay data is sorted in ascending
    72 expression value order and added to a matrix as columns. The matrix
    73 rows will contain mixed probes (also known as reporters or genes)
    74 decided by their rank. For each row in the matrix, the expression
    75 values are replaced with the row average value. Finally, each assay is
    76 reordered into its original order to retain a standard expression
    77 matrix were each row represents one probe. Assays are not mixed.
    78 
    79 Background subtraction and proper filtration should be done on the
    80 bioassay set before running this plug-in. The bioassay set must not
    81 contain any missing values.
     52Write me.
    8253
    8354The qQuantile normalization is inspired by the 'Cubic Spline'
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