2021
DOI: 10.1038/s41467-021-24584-w
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Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data

Abstract: As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiolog… Show more

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Cited by 45 publications
(76 citation statements)
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“…It is comprised in a blood transcriptional signature that is associated with neutrophil activation (Module M10.4 from the fixed BloodGen3 blood transcriptional module repertoire. 8 The goal is therefore to gather information that would permit to assess its potential as a blood transcriptional biomarker in various disease settings. Trainees may choose, for instance, to select other genes comprised in module M10.4, or in any of the 382 modules that form the BloodGen3 repertoire.…”
Section: Methodsmentioning
confidence: 99%
“…It is comprised in a blood transcriptional signature that is associated with neutrophil activation (Module M10.4 from the fixed BloodGen3 blood transcriptional module repertoire. 8 The goal is therefore to gather information that would permit to assess its potential as a blood transcriptional biomarker in various disease settings. Trainees may choose, for instance, to select other genes comprised in module M10.4, or in any of the 382 modules that form the BloodGen3 repertoire.…”
Section: Methodsmentioning
confidence: 99%
“…SLE-related public data were first downloaded from the Gene Expression Omnibus (GEO) database ( 19 , 20 ), and seven datasets were collected: three datasets from PBMC samples [GSE121239 (GPL13158, Normal: 20, SLE: 292) ( 21 , 22 ), GSE11907 (GPL96, Normal: 12, SLE: 110) ( 23 ), GSE81622 (GPL10558, Normal: 25, SLE: 30) ( 24 )]; four datasets from whole blood samples [GSE65391 (GPL10558, Normal: 72, SLE: 924) ( 25 ), GSE100163 (GPL6884, Normal: 14, SLE: 55) ( 26 28 ), GSE45291 (GPL13158, Normal: 20, SLE: 292) ( 29 ), GSE49454 (GPL1261, Normal: 20, SLE: 157) ( 30 )]. GEO2R was used for differential expression analysis of the GSE121239, GSE11907 and GSE81622 datasets.…”
Section: Methodsmentioning
confidence: 99%
“…(GPL10558, Normal: 25, SLE: 30) (24)]; four datasets from whole blood samples [GSE65391 (GPL10558, Normal: 72, SLE: 924) (25), GSE100163 (GPL6884, Normal: 14, SLE: 55) (26)(27)(28), GSE45291 (GPL13158, Normal: 20, SLE: 292) (29), GSE49454 (GPL1261, Normal: 20, SLE: 157) (30)]. GEO2R was used for differential expression analysis of the GSE121239, GSE11907 and GSE81622 datasets.…”
Section: Introductionmentioning
confidence: 99%
“…and obtained the complete transcriptome data of the datasets from GEO. The GSE100163 dataset was used as the training group, including 55 cSLE samples and 14 normal samples [14][15][16]; the GSE65391 dataset was used as the validation group, including 924 cSLE samples and 72 normal samples [17]. Also 232 autophagy-related genes were obtained from the previous top journals (Supplemental table 1).…”
Section: Data Downloadmentioning
confidence: 99%