Processes | Expression | KDMM Normalization

KDMM Normalization
The KDMM Normalization process (Kernel Density Mean of M component) is a scaling normalization method for RNA-seq data similar to TMM Normalization (Robinson and Oshlack 2010).
The data set is preprocessed and summarized into bins , exons , or genes at tall format with each row containing data from unique individual bin, exon, or gene across samples (columns). The M and A components between targeting sample (under normalization) and reference sample are calculated for estimating 2-dimensional Kernel Density and applying the density for weighted mean of M component as the scaling factor corresponding to the targeting sample.
Caution : This process can be computationally intensive for large data sets.
What do I need?
Two data sets are required to run this process.
The first data set, the Input Data Set , contains all of the numeric data to be analyzed. This data set must be in the tall format where each sample corresponds to one row and each column corresponds to a separate experimental condition or array.
The trimmed sam_mus_gse18905_ch1_6s.sas7bdat data set shown below lists SAM data from genes located on chromosome 1 from 3 different mouse lines.
The second data set is the Experimental Design Data Set (EDDS) . This required data set tells how the experiment was performed, providing information about the columns in the input data set. Note that one column in the EDDS must be named ColumnName and the values contained in this column must exactly match the column names in the input data set.
The edf_mus_gse18905_chr1-6s_sas7bdat EDDS, shown below, corresponds to the sam_mus_gse18905_ch1_6s.sas7bdat input data set.
The sam_mus_gse18905_ch1_6s.sas7bdat and edf_mus_gse18905_chr1-6s_sas7bdat data sets were downloaded from GEO .
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets .
Refer to the KDMM Normalization output documentation for detailed descriptions of the output of this process.