An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

Table 1 Description of the datasets used in the study

No.	GSE no.	GPL/platform	No. of sample		Cell type	Update (year)	Race
No.	GSE no.	GPL/platform	SLE patients	Controls	Cell type	Update (year)	Race
1	17755	1291/Hitachisoft	22	55	PBMC	2010	Japanese
2	12374	1291/Hitachisoft	11	6	PBMC	2012	Japanese
3	50772	570/Affymetrix	61	20	PBMC	2015	Unknown^a
4	81622	10558/Illumina	30	25	PBMC	2016	Unknown^b
5	121239	13158/Affymetrix	65^c	20	PBMC	2018	Caucasian/African American^d
6	126307	13369/Illumina	31	9	PBMC	2019	Several races^e

^aThe data were collected in the USA/South San Francisco, but the race of subjects is unknown
^bThe data were collected in the USA/Dallas, but the race of subjects is unknown
^cOnly the data related to the first visit (v1) samples of any patient were entered in the analysis
^dThis subset of the samples is derived from a preliminary dataset which were collected in the USA/Johns Hopkins University School of Medicine Institutional, and most of the race of subjects (92.8%) are Caucasian and African American
^eThe data collected in USA/Dallas, but the race of subjects is Australian, Australian (Irish/Scottish descent), born in India—ethnicity unknown, Caucasian, Caucasian/Japanese, English, Filipino, Indian, Iraqi, Latin American, Persian, Spanish, and White Australian/Anglo-Celtic

ISSN: 1478-6362