java application gsea v4.1.0  (Broad Institute Inc)

Journal: Biology of Sex Differences

Article Title: Integrative multi-omics characterization reveals sex differences in glioblastoma

doi: 10.1186/s13293-024-00601-7

Figure Lengend Snippet: Sex-specific transcriptome difference and pathway enrichment. A Analysis of differential gene expression in different sexes. The cutoff for log2fold change is 0.4, and the cutoff for p-value is 0.05. B Gene set enrichment analysis in different sexes. The gene set database used is the Human MSigDB, which includes Reactome, GO, and Wikipathways. C Single-sample gene set enrichment analysis (ssGSEA) of males and females. The significant gene sets are identified based on a p-value < 0.05. D The percentage of GBM transcriptional subtypes based on TCGA in patients (Wang et al.). Green represents MS (mesenchymal), purple represents PN (proneural), and cyan represents CL (classical). E The percentage of GBM pathway-based subtypes in patients (Garofano et al.). Red represents GPM (glycolytic/plurimetabolic), green represents MTC (mitochondrial), blue represents NEU (neuronal), and cyan represents PPR (proliferative/progenitor). F, G Immune cell type enrichment analysis of RNA expression data using the xCell tool. F Scores for three cell types. G Scores for immune cells that are important in GBM

Article Snippet: GSEAs were performed using the GSEA Java application, which was downloaded from the Broad Institute website ( https://www.gsea-msigdb.org/gsea/index.jsp ).

Techniques: Gene Expression, RNA Expression

Journal: Cells

Article Title: Identification of the Inner Cell Mass and the Trophectoderm Responses after an In Vitro Exposure to Glucose and Insulin during the Preimplantation Period in the Rabbit Embryo

doi: 10.3390/cells11233766

Figure Lengend Snippet: Significantly enriched gene sets (FDR < 0.05) in ICM and TE transcriptomes of in vitro-developed blastocysts with HI compared to CNTRL. Significantly enriched gene sets were identified by GSEA with the Molecular Signature Database (MSigDB) gene set collections: Hallmarks, KEGG, Reactome, and GO BP. GSEA was followed by SUMER analysis for gene set condensation. ( A ). Pie charts showing the enriched gene sets in HI ICM versus CNTRL ICM. ( B ). Pie charts showing the enriched gene sets in HI TE versus CNTRL TE. ICM, inner cell mass. TE, trophectoderm.

Article Snippet: Gene Set Enrichment Analysis (GSEA) [ ] was performed using the GSEA Java Desktop application (v4.0.3) from the Broad Institute.

Techniques: In Vitro

Journal: Cells

Article Title: Identification of the Inner Cell Mass and the Trophectoderm Responses after an In Vitro Exposure to Glucose and Insulin during the Preimplantation Period in the Rabbit Embryo

doi: 10.3390/cells11233766

Figure Lengend Snippet: Significantly enriched gene sets (FDR < 0.05) in ICM and TE transcriptomes of in vitro-developed blastocysts with HG compared to CNTRL. Significantly enriched gene sets were identified by GSEA with the Molecular Signature Database (MSigDB) gene set collections: Hallmarks, KEGG, Reactome, and GO BP. GSEA was followed by SUMER analysis for gene set condensation. ( A ). Pie charts showing the enriched gene sets in HG ICM versus CNTRL ICM. ( B ). Pie charts showing the enriched gene sets in HG TE versus CNTRL TE. ICM, inner cell mass. TE, trophectoderm.

Article Snippet: Gene Set Enrichment Analysis (GSEA) [ ] was performed using the GSEA Java Desktop application (v4.0.3) from the Broad Institute.

Techniques: In Vitro

Journal: Cells

Article Title: Identification of the Inner Cell Mass and the Trophectoderm Responses after an In Vitro Exposure to Glucose and Insulin during the Preimplantation Period in the Rabbit Embryo

doi: 10.3390/cells11233766

Figure Lengend Snippet: Significantly enriched gene sets (FDR < 0.05) in the ICM and TE transcriptomes of in vitro-developed blastocysts with HGI compared to CNTRL. Significantly enriched gene sets were identified by GSEA with the Molecular Signature Database (MSigDB) gene set collections: Hallmarks, KEGG, Reactome, and GO BP. GSEA was followed by SUMER analysis for gene set condensation. ( A ). Pie charts showing the enriched gene sets in HGI ICM versus CNTRL ICM. ( B ). Pie charts showing the enriched gene sets in HGI TE versus CNTRL TE. ICM, inner cell mass. TE, trophectoderm.

Article Snippet: Gene Set Enrichment Analysis (GSEA) [ ] was performed using the GSEA Java Desktop application (v4.0.3) from the Broad Institute.

Techniques: In Vitro

Journal: American Journal of Cancer Research

Article Title: Elevated bile acid metabolism and microbiome are associated with suppressed cell proliferation and better survival in breast cancer

doi:

Figure Lengend Snippet: GSEA and cell fractions of the high bile acid metabolism group. A. Dot plots show the gene sets that are significantly enriched in the high bile acid metabolism group (upper quartile) by GSEA in the TCGA, METABRIC, and GSE96058 cohorts. The horizontal scale and the color of each dot indicate normalized enrichment score (NES), and the size of each dot indicates false discovery rate (FDR). FDR less than 0.25 is considered significant. B. Box-and-whisker plots show the cell fractions of adipocytes, preadipocytes, vascular endothelial cell (VEC), lymphatic endothelial cell (LEC), and mesangial endothelial cells (MEC) according to the high and low bile acid scores (upper and lower quartiles) in the TCGA, METABRIC, and GSE96058 cohorts. All two-group comparisons were performed using the Wilcoxon signed-rank test. The error bars in each boxplot show the 95% confidence interval. The line in the box shows the median, and the top and bottom show the 25th and 75th percentiles respectively.

Article Snippet: The analysis was performed using the java application GSEA v4.1.0 provided by the Broad Institute, and FDR less than 0.25 was considered significant according to recommendations.

Techniques: Whisker Assay

Journal: American Journal of Cancer Research

Article Title: Elevated bile acid metabolism and microbiome are associated with suppressed cell proliferation and better survival in breast cancer

doi:

Figure Lengend Snippet: The association of bile acid metabolism and Nottingham histological grade, MKi67 expression, Hallmark cell proliferation-related gene sets, intratumoral heterogeneity, HRD, and mutation rates. A. Box-and-whisker plots of bile acid metabolism score by Nottingham histological grade in the TCGA, METABRIC, and GSE96058 cohorts. Scatter plots show the correlation between MKI67 gene expression and the score. B. Dot plots show the gene sets significantly enriched in the low bile acid metabolism group (lower quartile) by GSEA in the TCGA, METABRIC, and GSE96058 cohorts. The horizontal scale and the color of each dot indicate normalized enrichment score (NES), and the size of each dot indicates false discovery rate (FDR). FDR less than 0.25 was considered significant. C. Box-and-whisker plots show intratumoral heterogeneity, homologous recombination defects (HRD), silent or non-silent mutation rate, and proliferation scores according to the high and low bile acid scores (upper and lower quartiles) in TCGA. Multiple group comparisons were performed using the Kruskal-Wallis test and two-group comparisons were performed using the Wilcoxon signed-rank test. The error bars in each boxplot show the 95% confidence interval. The line in the box shows the median, and the top and bottom show the 25th and 75th percentiles respectively.

Article Snippet: The analysis was performed using the java application GSEA v4.1.0 provided by the Broad Institute, and FDR less than 0.25 was considered significant according to recommendations.

Techniques: Expressing, Mutagenesis, Whisker Assay, Gene Expression, Homologous Recombination

Journal: American Journal of Cancer Research

Article Title: Elevated bile acid metabolism and microbiome are associated with suppressed cell proliferation and better survival in breast cancer

doi:

Figure Lengend Snippet: GSEA by bacterial species specific to high and low bile acid metabolism groups. (A) Dot plots show the gene sets significantly enriched by GSEA in groups with high or low abundances (median cutoff) of Gammaretrovirus, Hymenobacter, Anaerococcus, and Collimonas which were specific to the high bile acid metabolism group in TCGA. (B) Dot plots show the gene sets significantly enriched by GSEA in groups with high or low abundances (median cutoff) of Lactobacillus, Ruegeria, and Marichromatium which were specific to the low bile acid metabolism group in TCGA. The horizontal scale and the color of each dot indicate normalized enrichment score (NES) (high values are shown in red and low in blue in A, and vice versa in B), and the size of each dot indicates false discovery rate (FDR). FDR less than 0.25 is considered significant.

Article Snippet: The analysis was performed using the java application GSEA v4.1.0 provided by the Broad Institute, and FDR less than 0.25 was considered significant according to recommendations.

Techniques:

Journal: bioRxiv

Article Title: SARS-CoV-2 Airway Infection Results in Time-dependent Sensory Abnormalities in a Hamster Model

doi: 10.1101/2022.08.19.504551

Figure Lengend Snippet: A) Mechanical thresholds of mock, IAV, and SARS-CoV-2 animals at 28dpi (n=6 per group; **p<0.01, ****p<0.0001 for one-way ANOVA Tukey’s m.c.). B) Volcano plot for 31dpi SARS-CoV-2 tDRG RNA-seq (n=3 per group). Red=p-adj.<0.1, log2FC>0. Blue=p-adj.<0.1, log2FC>0. Green=p-nom.<0.05. C) IPA top 10 canonical pathways (−log10(p-value)>1.3) associated with 31dpi SARS-CoV-2 tDRG DEGs (p-nom.<0.05). D) enrichr DisGENET gateway top 10 diseases associated with 31 dpi SARS-CoV-2 tDRG DEGs (p-nom.<0.05). E) Log 2 (FC) of select neuronal and inflammatory genes from 31dpi RNA-seq (p-adj.<0.1). F) Positively and negatively enriched cell subtypes associated with 31dpi SARS-CoV-2 tDRG DEGs (GSEA NES>|1.5|; DEG p-adj.<0.1). G) IPA top 15 upstream regulators between 31dpi SARS-CoV-2 tDRG, Striatum, and Thalamus (DEG p-nom.<0.05).

Article Snippet: Gene set enrichment analyses were conducted using the GSEA Java application for Mac (v 4.1.0) (MSigDB; Broad Institute, UC San Diego).

Techniques: RNA Sequencing