Shambhala2 is a harmonizer of gene expression profiles obtained via both mRNA microarray hybridization ane next-generation sequencing.
It coverts all the profiles one-by-one, independently one from another, to a predefined shape that is set by the defnitive dataset Q.
To provide robust result, such conversion is preformed within the auxiliary calibration dataset P.
One-by-one, each profile that needs to be harmonized, is first quantile-normalized (Bolstad et al., 2003, doi:10.1093/bioinformatics/19.2.185) with the dataset P. Then the result, P_prim is normalized according to CuBlock method (Junet et al, 2021, doi:10.1093/bioinformatics/btab105). Then, from the CuBlock-normalized result (P_bis), we take only the profile to be harmonized, and rescale gene expression level. to set the mean and standard deviation of the expression level of each gene g equal to mean value and standard deviation of gene g expression level in the definitive dataset Q.
After it, we repeat the procedure for all other profiles that have to be harmonized.
The deposited R functuion
Shambhala2 = function(InputFileName, PFileName, QFileName, delete_buffer_files = TRUE, k = 5 ) calls MATLAB (R) applications Shambhala2.m, CuBock.m and readExpressionData.m.
k is the number of probe clusters for the application of k-means to find probe-cluster partitions. By default it is 5.
R: requires matrixStats package:
install.packages("matrixStats")
MATLAB (R) requires method quantilenorm (https://www.mathworks.com/help/bioinfo/ref/quantilenorm.html)
from the Bioinformatics Toolbox (https://www.mathworks.com/help/bioinfo/index.html?s_tid=CRUX_lftnav)
MATLAB (R) licence is free for educational institutions. Those who do not have access to MATLAB (R) licence, may try to use GNU Octave instead.
Please note that execution of code may be time-consuming due to harmonization if each profile one-by-one. For example, processing the file Input.csv takes about two minutes at an Ubuntu 20.04.2 LTS machine with Intel® Core™ i7-7700K CPU @ 4.20GHz × 8 CPU's and 31,2 GiB or RAM. Therefore, parallel execution of the code may be a good idea.
The function returns a harmonized gene expression matrix