Changeset 1065

Timestamp:

May 14, 2009, 12:28:59 PM (14 years ago)

Author:

Jari Häkkinen

Message:

Reverting changes commit by mistake to README.

File:

• plugins/base2/net.sf.basedb.normalizers/trunk/README (modified) (1 diff)

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

@@ -50 +50 @@
 == qQuantile normalization ==
 
-qQN geometric mean non-logged values!
-qQN must have positive numbers!
-
-The assays are normed against a selectable sub-set of the assays
-... If a probe has no well-defined measurement (i.e., no assay in
-the reference has a well defined value for a probe) it is simply
-ignored from the target distribution.
-
-Ja, jag tankte att man far justera q till maximalt antal icke nan ...
-men som sagt an sa lange ar q==100.
-
-Den begränsning som finns nu är att det måste finnas lagom många
-väldefinierade mätvärden per assay (några hundra per assay) annars
-kraschar nog programmet. Jag kommer att lösa detta genom att välja
-antalet bins i distributionsberäkningen som #bins=max(100,N_i/10)
-i=1...#assays och kräva att #bins är minst 10. Kravet är alltså att
-varje assay måste ha minst 100 väldefinierade punkter. Om inte kravet
-är uppfyllt stannar programmet med ett någorlunda trevligt meddelande.
-
-In q-quantile normalization each assay data is sorted in ascending
-expression value order and added to a matrix as columns. The matrix
-rows will contain mixed probes (also known as reporters or genes)
-decided by their rank. For each row in the matrix, the expression
-values are replaced with the row average value. Finally, each assay is
-reordered into its original order to retain a standard expression
-matrix were each row represents one probe. Assays are not mixed.
-
-Background subtraction and proper filtration should be done on the
-bioassay set before running this plug-in. The bioassay set must not
-contain any missing values.
+Write me.
 
 The qQuantile normalization is inspired by the 'Cubic Spline'