A robust normalization method for zero-inflated count data such as microbiome sequencing data.
GMPR(OTUmatrix, min_ct = 2, intersect_no = 4)A vector of GMPR size factor for each sample.
An OTU count table, where OTUs are arranged in rows and samples in columns.
The minimal number of OTU counts. Only those OTU pairs with at least min_ct counts are
considered in the ratio calculation. The default is 2.
The minimal number of shared OTUs between samples. Only those sample pairs sharing at least intersect_no OTUs
are considered in geometric mean calculation. The default is 4.
Jun Chen and Lujun Zhang
Normalization is a critical step in microbiome sequencing data analysis to account for variable library sizes. Microbiome data contains a vast number of zeros, which makes the traditional RNA-Seq normalization methods unstable. The proposed GMPR normalization remedies this problem by switching the two steps in DESeq2 normalization:
First, to calculate rij, the median count ratio of nonzero counts between samples: rij=median(cki/ckj) (k in 1:OTU_number and cki, ckj is the non-zero count of the kth OTU)
Second, to calculate the size factor si for a given sample i: si=geometric_mean(rij)
Li Chen, James Reeve, Lujun Zhang, Shenbing Huang, and Jun Chen. 2018. GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ, 6, e4600.
data(throat.otu.tab)
size.factor <- GMPR(t(throat.otu.tab))
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