Kuijjer Lab

New BioRχiv pre-print

June 28, 2022

We just posted a new version of our previous BioRχiv pre-print on normalization for network modeling by Ping-Han. Ping-Han found that certain normalization methods, in specific quantile-based methods, can introduce false positive associations in co-expression measurements. However, there methods can be very powerful. Smooth quantile normalization, for example, allows one to normalize heterogeneous data, while keeping global expression differences between different subgroups of samples. Ping-Han therefore developed a new algorithm, called SNAIL, to correct for these false-positive associations, which allows for more precise comparative network analysis in large-scale heterogeneous data. More information on the pre-print can be found here.

Schematic overview of SNAIL and the analyses presented in the preprint. SNAIL is based on the smooth quantile normalization algorithm. However, it uses the trimmed mean to derive the quantile distribution for all samples as well as for every biological group of samples. In addition, SNAIL uses the median of the quantiles to normalize the expression for genes with the same read count in one sample. To evaluate SNAIL, we compared the Spearman’s correlation coefficients between genes and the edge weights in sample-specific networks with the ground truth, derived from expression normalized using spike-in genes.