Human gene 1.1 st array

We selected GEO (http://www.ncbi.nlm.nih.gov/geo), which is a publically available database of gene/microarray profiles for our study.
The search strategy (‘bariatric surgery’ [MeSH Terms] OR bariatric surgery [All Fields]) AND (‘Homo sapiens’[Organism] AND ‘Expression profiling by array’[Filter]) was adopted.
Inclusion criteria were as follows: (i) subcutaneous AT after bariatric surgery from subjects with obesity; (ii) subcutaneous adipose tissue before bariatric surgery used as controls. We extracted the gene expression profiles GSE29409 [18 (link)] and GSE59034 [19 (link)] from the GEO database. The platform for GSE29409 is GPL7020, NuGO array (human) NuGO_Hs1a520180, which includes five samples of subcutaneous AT that were previously obtained from obese subjects, and five further subcutaneous AT samples in short term after bariatric surgery. The platform for GSE59034 was GPL11532 [HuGene-1_1-st] Affymetrix Human Gene 1.1 ST Array [transcript (gene) version], which includes subcutaneous AT samples from obese subjects before (n = 16) and after short-term bariatric surgery (n = 16). Data table header descriptions and series matrix files of GSE29409 and GSE59034 were downloaded.

We present peripheral blood monocyte eQTLs for TNFSF15 from a previous study using samples from 39 healthy individuals and 80 patients with IBD [19 (link)], as well as a study using 45 patients with anti-neutrophil cytoplasmic antibody-associated vasculitis [18 (link)]. Gene expression data was measured on the Affymetrix Human Gene 1.1 ST Array. Microarray mRNA expression was normalized by robust multiarray averaging using the oligo package [71 (link)] and adjusted with PEER [72 (link)]. IBD patient and healthy control genotypes were measured using the Illumina Human OmniExpress12v1.0 BeadChip, and vasculitis patients were genotyped using the Affymetrix SNP6.0 Array. eQTL testing was performed using a score test implemented in the GGtools Bioconductor package [73 (link)]. Genomic locus plots were generated using the Gviz Bioconductor package [74 (link)] with RefSeq annotation for genes in the hg19 genome build.

The dataset GSE44265 provided by Sawaya BE. et al. (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44265 (accessed on 1 December 2021)) [44 (link)] was downloaded from the GEO database. In the dataset, GSM1045807 and GSM1045808 are differentiated SH-5Y cells treated with supernatant from HIV (clade B) infected U-937; GSM1045805 and GSM1045806 are normal differential SH-5Y cells as the negative control group. All samples were hybridized on the Affymetrix Human Gene 1.1 ST Array (Affymetrix, Santa Clara, CA, USA). All of the raw data were processed using affy and related R packages with Robust Multi-array Average approach for background normalization as per the package instruction [54 (link)]. Differentially expressed genes (DEGs) were selected according to changing folds more than 2 and a p value less than 0.05. The DEGs were divided into the upregulated DEG group and the downregulated DEG group.

Data is openly available (E-GEOD-49065) and were originally published by Steegenga et al., who studied age-induced changes in DNA methylation and their effect on gene expression. Methylation data was measured using the Illumina Infinium 450 K BeadChip and expression data with the Affymetrix Human Gene 1.1. ST array. The cohort of methylation and expression data in peripheral blood mononuclear cells (PBMCs) included 10 healthy Caucasian male blood donors, aged 30–66 years^{28 (link)}.

For microarrays hybridization, RNAs were purified using the RNeasy Micro kit (Qiagen), and quality were measured by Bioanalyzer technology. cDNA was generated from 300 ng of total RNA using the Ambion WT Expression Kit (Applied Biosystems) and according to manufacturer's instructions.
Fragmented, labeled and amplified cDNA was hybridized to the Human Gene 1.1 ST Array (Affymetrix), which represents approximately 33,000 probe sets. Wash, rinse and scanning of the arrays was performed according to manufacturer's instructions. Quality of the microarray was assessed by dChip [30 ] and Expression Console (Affymetrix) softwares.
Raw expression data from microarrays were normalized using the robust multiarray algorithm [31 (link)] with a custom probe set definition that mapped probes to Entrez Gene IDs (HuGene11stv1_Hs_ENTREZG) [32 (link)]. To identify differentially expressed genes between the different microarray study groups we employed Significant Analysis of Microarray [33 (link)]. One thousand permutations of the data were used to estimate the False Discovery Rate (FDR) and to select differentially expressed genes.
The CEL files and RMA values were deposited on Gene Expression Omnibus (GSE66532).

CENPE expression in human patients with medulloblastoma was downloaded from Gene Expression Omnibus (GEO) dataset GSE85218 [53 (link)], generated on the Affymetrix Human Gene 1.1 ST Array platform (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85218, accessed on 15 February 2021). Data are shown as RSEM count.
Overall Survival (OS) data were obtained from [53 (link)] and maximal survival time was cut at 10 years; patients without survival data were excluded. Patients were divided in two groups, “low CENPE” and “high CENPE” using median expression as the discriminant value. Log-rank (Mantel-Cox) test was used to compare survival between groups.

The major reference of this Canadian dataset is Calvalli et al.^{15 (link)}. The normalized and log-transformed gene expression levels of 763 medulloblastoma patients, quantified by the Affymetrix Human Gene 1.1 ST Array platform, were downloaded from Gene Expression Omnibus archive (ID: GSE85217). The clinical data were downloaded from the from the website (https://ars.els-cdn.com/content/image/1-s2.0-S1535610817302015-mmc2.xlsx)^{15 (link)}, which contained the censored/non-censored time-to-death information of 612 patients. The gene expression levels and the clinical data were joined for survival analysis.