Changes between Initial Version and Version 1 of SOPs/normalizePublic

Timestamp:

01/23/13 16:49:43 (12 years ago)

Author:

trac

Comment:

--

SOPs/normalizePublic

@@ +1 @@
+== Normalizing From the Same Platform ==
+
+If the arrays of interest are of the same platform, follow the same procedure as outlined in [wiki:SOPs/normalize_ma normalizing microarrays].
+
+== Normalizing From Different Platforms ==
+
+If the samples for normalizing are from different platforms,
+
+
+=== Quantile ===
+
+1. Normalize arrays that belong to the same platform together
+2. Once normalized platform-specific, quantile normalize to produce the same distribution across all arrays.
+ * Quantile is the easiest to perform and will ensure the samples have the same distribution. Only genes common to all platforms should be selected. After normalization, check if normalization worked by clustering the samples, or by PCA. If samples cluster by a batch effect (eg. source) or platform, then quantile normalization was not adequate. For discussion on cross-platform normalization see,
+  * [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1904274/ Three methods for optimization of cross-laboratory and cross-platform microarray expression data]
+  * [http://www.genomeweb.com/node/919125#q6 Genome Technology - How do you compare array data across different platforms?]
+
+ 
+{{{
+  library(limma)
+  # Read the data
+  data = read.delim(myFile)
+  # Quantile normalize 
+  data.quantile_normalize = normalizeQuantiles(data[,c(2:ncol(data))])
+}}}
+
+
+=== Other ===
+
+ * The above method may not remove batch effects (ie. samples clustering by platform)
+ * Several methods are available to remove batch effects: 
+   * [http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0017238  Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods.] 
+   * [http://biostatistics.oxfordjournals.org/content/8/1/118.abstract Adjusting batch effects in microarray expression data using empirical Bayes methods], as implemented by [http://www.bu.edu/jlab/wp-assets/ComBat/Abstract.html ComBat], is recommended in the study comparing several methods for removing batch effects.