Expression profiles of endometriosis-associated mRNAs in GSE7305, GSE120103, GSE7307 and GSE51981 were downloaded from Gene Expression Omnibus (GEO) database. The microarray datasets GSE7305 and GSE120103 with complete clinical information and same menstrual cycle were used as training sets to identify hub differentially expressed genes (DEGs) of endometriosis, GSE7307 and GSE51981 were used as test sets to validate our results, respectively. Dataset GSE7305 7 (link) performed on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) was used to recognize hub DEGs in ovarian endometrioma, which includes 10 ovarian endometriomas from women with endometriosis (EC) and 10 normal endometria (Ctrl). Dataset GSE120103 8 performed on the GPL6480 platform (Agilent-014850 Whole Human Genome Microarray 4x44K G4112F) was applied to identify hub DEGs in eutopic endometrium, which includes 9 eutopic endometria from fertile women with endometriosis (EU) and 9 Ctrl. Dataset GSE7307 performed on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) was used to validate EC-associated hub DEGs, which includes 23 EC and 18 Ctrl. And Dataset GSE51981 performed on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) was used to validate EC-associated hub DEGs, which includes 38 EU and 71 Ctrl.
Human genome u133 plus 2.0 array
Manufactured by Thermo Fisher Scientific
Sourced in United States, China, United Kingdom
The Human Genome U133 Plus 2.0 Array is a high-density oligonucleotide microarray designed to analyze the expression of over 47,000 transcripts and variants from the human genome. It provides comprehensive coverage of the human transcriptome and is suitable for a wide range of gene expression studies.
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Lab products found in correlation
Human genome u133a array, by Thermo Fisher Scientific (47 mentions)
Human genome u133a 2.0 array, by Thermo Fisher Scientific (35 mentions)
Rneasy mini kit, by Qiagen (19 mentions)
Human gene 1.0 st array, by Thermo Fisher Scientific (15 mentions)
Trizol, by Thermo Fisher Scientific (13 mentions)
Agilent 014850 whole human genome microarray 4x44k g4112f, by Agilent Technologies (12 mentions)
Human exon 1.0 st array, by Thermo Fisher Scientific (12 mentions)
Trizol reagent, by Thermo Fisher Scientific (12 mentions)
Genechip scanner 3000 7g, by Thermo Fisher Scientific (12 mentions)
Agilent 2100 bioanalyzer, by Agilent Technologies (11 mentions)
Humanht 12 v4.0 expression beadchip, by Illumina (11 mentions)
Gpl570 platform, by Thermo Fisher Scientific (10 mentions)
Humanwg 6 v3.0 expression beadchip, by Illumina (10 mentions)
Humanht 12 v3.0 expression beadchip, by Illumina (9 mentions)
Genechip scanner 3000, by Thermo Fisher Scientific (9 mentions)
Hiseq 2500, by Illumina (8 mentions)
Humanmethylation450 beadchip, by Illumina (8 mentions)
Genechip 3 ivt express kit, by Thermo Fisher Scientific (8 mentions)
Genespring gx software, by Agilent Technologies (7 mentions)
Mouse genome 430 2.0 array, by Thermo Fisher Scientific (7 mentions)
1 286 protocols using human genome u133 plus 2.0 array
1
Endometriosis-associated mRNA Expression
2
Transcriptome Analysis of Breast Cancer
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The datasets were downloaded from the GEO. The datasets GSE45827 and GSE65216 were provided by the Institut Curie in France. The transcriptome analyses of the human breast cancer samples and adjacent normal breast tissue samples in these datasets were performed with Affymetrix Human Genome U133 Plus 2.0 Arrays [11 (link)]. GSE102484 was provided by the Koo Foundation SYS Cancer Centre in Taiwan. Expression profiling for this dataset was also conducted with an Affymetrix Human Genome U133 Plus 2.0 Array. According to the official description of GSE45827, the dataset contains 130 primary invasive breast cancer samples, including 126 invasive ductal carcinomas and 4 medullary carcinomas (41 triple-negative (TN), 30 HER2+, 29 Luminal A, and 30 Luminal B samples), as well as 11 normal tissue samples and 14 cell lines. GSE65216 (comprising GSE65194 and GSE65212) contains 306 primary invasive breast cancer samples, including 302 invasive ductal carcinomas and 4 medullary carcinomas (110 TN, 78 HER2+, 58 Luminal A, and 60 Luminal B samples), as well as 22 normal tissue samples and 14 cell lines. Only GSE65194 has related clinical data. GSE102484 contains only 683 primary breast cancer tissue samples.
3
Microarray Analysis of Parkinson's Disease
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Publicly available microarray data from PDD (n = 13), PDND (n = 15), and HCs (n = 14) were obtained from an earlier study [PMID:18649390] [25 (link)]. The preprocessed and normalized data were acquired from the Affymetrix Human Genome U133 Plus 2.0 Array. To ensure consistency, the public data were renormalized in R using the normalize quantile function in preprocessCore. Three other independent datasets (GSE7621, GSE20141, GSE49036) on SN tissue from the postmortem brains of HCs (n = 25) and PD patients (n = 45) were pooled to validate the data from [25 (link)]. Samples were run on the Affymetrix Human Genome U133 Plus 2.0 Array and processed in R using the ReadAffy parser in affy. Preprocessing and normalization were performed with the frma package using the “robust weighted average” option for probe summarization.
Microarrays of Ifnb+/+ and Ifnb–/– CGN cultures with or without 24-h rIFN-β treatment (100 U/ml) were set up in triplicate, and the extracted cDNA was applied to the Affymetrix Mouse Genome 430 2.0 microarray chip (SCIBLU, Affymetrix). GEO accession number GSE63815. Data were analyzed with Arraystar 3 (DNA STAR Inc.) and quantile-normalized and processed using the RMA (Affymetrix) algorithm. Data were preprocessed in R using the affy package and RMA function.
Microarrays of Ifnb+/+ and Ifnb–/– CGN cultures with or without 24-h rIFN-β treatment (100 U/ml) were set up in triplicate, and the extracted cDNA was applied to the Affymetrix Mouse Genome 430 2.0 microarray chip (SCIBLU, Affymetrix). GEO accession number GSE63815. Data were analyzed with Arraystar 3 (DNA STAR Inc.) and quantile-normalized and processed using the RMA (Affymetrix) algorithm. Data were preprocessed in R using the affy package and RMA function.
4
Validating Gene Expression Profiles in AML
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For validation analysis we included three independent gene expression datasets from two studies, one from The Cancer Genome Atlas (TCGA) [26 (link)] and two (GSE12417-GPL96 and GSE12417-GPL570) from the study of Metzeler et al. [17 (link)]. The TCGA dataset was composed of gene expression profiles from 197 AML samples (97 CN) achieved by Affymetrix Human Genome U133 Plus 2.0 Array. Level-2 data, which were probe-level pre-normalized signals processed by TCGA, were downloaded and transformed into Log-2 scale. The two datasets from the study by Metzeler et al., including 163 and 79 CN-AML patients, respectively, were profiled with Affymetrix Human Genome U133 Plus 2.0 Array. We used the authors' pre-processed datasets deposited in the Gene Expression Omnibus (GEO, accession ID GSE12417) [57 (link)]. In the three datasets, each of the genes with multiple probes was represented by the most “informative” probe that carried the largest coefficient of variation, defined as the ratio of per-probe standard deviation to per-probe average.
5
Analysis of Melanoma Transcriptomes
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The GEO database (http://www.ncbi.nlm.nih.gov/geo/ ) is a public database used to host high-throughput microarray and next-generation sequence functional genomic datasets32 (link). We downloaded expression profiles of patients with SKCM with clinical data from the GEO database. For this part of the study, we selected the datasets GSE15605 and GSE11444533 (link),34 (link). The data of GSE15605 were obtained with the GPL570 Platforms (Affymetrix Human Genome U133 Plus 2.0 Array) by Vanderbilt University, and came from 46 primary melanoma samples, 12 regional or distant metastases, and 16 normal skin samples. In this dataset, we only selected 46 primary melanoma samples and 16 normal skin samples for subsequent analysis. Similarly, the data of GSE114445 were based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). We analyzed 16 primary melanoma tissues and 6 normal skin tissues in the GSE114445 dataset. We also selected the dataset with accession number GSE123139, which includes data from single-cell transcriptional analysis of immune cells from human melanoma tumors35 (link). The samples were obtained from public databases and the study was carried out in accordance with relevant guidelines/regulation. The statement of ethics approval and informed consent were not needed for this study.
6
Integrated Analysis of Medulloblastoma Gene Expression
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We downloaded the gene expression profile data for GSE42656 , GSE74195 , GSE109401 , and GSE50161 from the GEO database (http://www.ncbi.nlm.nih.gov/geo ). The GSE42656 data set was based on the GPL6947 Platforms (Illumina HumanHT−12 V3.0 expression beadchip) and contained nine MB samples and 16 normal brain samples (Henriquez et al., 2013 (link)). The GSE74195 data set was based on the GPL570 Platform (Affymetrix Human Genome U133 Plus 2.0 Array) and contained 27 MB and five normal brain tissue samples (De Groot et al., 2011 (link)). The GSE50161 data set was based on the GPL570 Platform (Affymetrix Human Genome U133 Plus 2.0 Array) and included 22 MB samples and 13 normal brain tissues (Griesinger et al., 2013 (link)). The GSE109401 data set was based on GPL16686 Platforms (Affymetrix Human Gene 2.0 ST Array (transcript (gene) version)) and included 19 medulloblastoma samples and five normal brain samples (Rivero-Hinojosa et al., 2018 (link)). We selected these four gene expression profiles for further integrated analyses to avoid racial differences and errors in individual experiments.
7
Differential Gene Expression Analysis of ccRCC
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Expressing profiles of mRNA data of ccRCC cancer were downloaded from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/ ). Dataset GSE36895 (Affymetrix Human Genome U133 Plus 2.0 Array), Dataset GSE53757 (Affymetrix Human Genome U133 Plus 2.0 Array) and dataset GSE6344 (Affymetrix Human Genome U133A Array) were used for validation 15 (link), 16 (link). For the microarray analyses, we used RMA background correction for the raw expression data at first, and log2 transformation and normalization were performed for processed signals. Then we used “affy” R package to summarize the median-polish probe sets. Probes were annotated by the Affymetrix annotation files. Limma package in R package was used to screen the differentially expressed genes (DEGs) between tumor tissues and normal ones.
8
Differential Gene Expression in Sepsis
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GSE95233 and GSE28750 expression profile data were downloaded from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/
9
Profiling Blood Cell Type-Specific Genes
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We used a set of genes marked as differentially expressed among major blood cell types from Newman et al. (2015) (link). The list contained 372 genes with expression patterns identifying multiple blood cell types including: B cells, T cells, Monocytes, Neutrophils, and Natural Killers (NK) in different states (see Supplemental File S12 for list of genes for each cell type). We obtained cell-type expression profiles from GSE22886 (Abbas et al. 2005 (link)).
We used three bulk tissue data sets: (1) GTEx-whole-blood (GTEx-blood) data set from human (version 6, count of samples 393); (2) normal samples from microarray data set GSE27562 from LaBreche et al. (2011) (link) (DS1, platform: Affymetrix Human Genome U133 Plus 2.0 Array, sample count: 31); and (3) microarray data set GSE16028 from Karlovich et al. (2009) (link) (DS2, platform: Affymetrix Human Genome U133 Plus 2.0 Array, sample count:105). Both Affymetrix data sets were preprocessed as described in Farahbod and Pavlidis (2019) (link). R2 values were obtained from all three data sets using a PCR method with seven first component scores. Similar to the brain, we performed network clustering on GTEx-whole-blood network using WGCNA.Supplemental File S13 contains cluster labels and R2 values for the genes. Functional enrichment analysis for clusters was performed similar to the brain.
We used three bulk tissue data sets: (1) GTEx-whole-blood (GTEx-blood) data set from human (version 6, count of samples 393); (2) normal samples from microarray data set GSE27562 from LaBreche et al. (2011) (link) (DS1, platform: Affymetrix Human Genome U133 Plus 2.0 Array, sample count: 31); and (3) microarray data set GSE16028 from Karlovich et al. (2009) (link) (DS2, platform: Affymetrix Human Genome U133 Plus 2.0 Array, sample count:105). Both Affymetrix data sets were preprocessed as described in Farahbod and Pavlidis (2019) (link). R2 values were obtained from all three data sets using a PCR method with seven first component scores. Similar to the brain, we performed network clustering on GTEx-whole-blood network using WGCNA.
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