Bovine blood gamma/delta T cell stimulation study
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ABSTRACT:
ORGANISM(S): Bos taurus
PROVIDER: GSE1870 | GEO | 2004/10/28
SECONDARY ACCESSION(S): PRJNA90541
REPOSITORIES: GEO
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Project description:Blood gamma/delta T cells were purfiied from holstein calves less than six months old. Blood from 4 individual calves were pooled and gamma/delta T cells were purified by immunomagnetic separation, rested in RPMI medium for 12h, and underwent ConA & hIL-2 stimulation. Total RNAs were extracted by using Trizol reagents. 15 ug of total RNA was used for cDNA synthesis and microarray hybridization. Table 1: regulated genes These genes were identified, in GeneSpring software, via filtering of genes with relative expression ratio of 1.5 and over or 0.667 or below, equivalent to up- and down-regulation by fold change 1.5 or more, in both experiments. Table 2: down-regulated genes These genes were identified, in GeneSpring software, via filtering of genes with relative expression ratio of 0.667 or below, equivalent to down-regulation by fold change 1.5 or more, in both experiments. The avergae fold change was then converted to negative value. Gene annotation was performed by using GeneSpring's "Build Simplified Ontology" constructor. Table 3: up-regulated genes These genes were identified, in GeneSpring software, via filtering of genes with relative expression ratio of 1.5 or over, equivalent to up-regulation by fold change 1.5 or more, in both samples. Gene annotation was performed by using GeneSpring's "Build Simplified Ontology" constructor.
2010-06-05 | E-GEOD-1870 | biostudies-arrayexpress
Project description:In total, 5,840 protein groups were identified, among which 5,188 proteins were quantified. The fold-change cutoff was set when proteins with quantitative ratios above 1.5 or below 0.667 are deemed significant. Among the quantified proteins, 95 proteins are up-regulated and 155 proteins are down-regulated in group gr1-seedling/WT-seedling.
2019-11-12 | PXD011814 | Pride
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-24 | E-MTAB-9167 | biostudies-arrayexpress
Project description:This series includes the four major subtypes of pituitary adenomas and normal post-mortem pituitary tissue Data Transformation Using Affymetrix Microarray Suite 5.0 global scaling was applied to the quantification data to adjust the average recorded to a target intensity of 100. Data were then exported into the bioinformatics software GeneSpring 6.0 (Silicon Genetics, Redwood City, CA) for further analysis. Data normalization was performed to scale the data so that the average intensity value on each array was 1 by dividing each expression value by the median of the expression levels on each chip. The individual gene expression levels for each of the 4 pituitary adenoma subtype arrays was divided by the expression level in the normal pituitary array. Thus, the data are presented as relative to the expression in normal pituitary tissue. Filtering was then performed to identify genes over-expressed or under-expressed at least 2.0 fold in tumours compared to normal pituitary. TABLE 1: The genes / ESTs differentially overexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given. TABLE 2: The genes / ESTs differentially underexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given. Keywords = pituitary tumor Keywords: other
2005-05-23 | GSE2175 | GEO
Project description:LncRNA expression profiling for liver tissues of mice fed for a normal diet (NFD, 3mice) and a high-fat diet (HFD, 3mice) Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-23 | E-MTAB-8730 | biostudies-arrayexpress
Project description:Purpose: The goals of this study are to compare the phenotypic, transcriptomic, trafficking and functional atttibutes of Influenza-experienced circulating or resident memory phenotype cells from the spleen or lung-draining mediastinal lymph nodes. Results: Following read alignment, gene expression profiles were computed using featureCounts. Filtering and visualization of differentially expressed genes with a log2fold change >1.5<-1.5 and a p value of less than 0.05 were identified using Partek GS software and for some figures values from Partek were normalized and displayed using GraphPad Prism. Gene set enrichment and functional assignment were performed in DAVID bioinformatics resources and software from the Broad Institute.
2021-06-28 | GSE172279 | GEO
Project description:This series includes the four major subtypes of pituitary adenomas and normal post-mortem pituitary tissue; Data Transformation; Using Affymetrix Microarray Suite 5.0 global scaling was applied to the quantification data to adjust the average recorded to a target intensity of 100. Data were then exported into the bioinformatics software GeneSpring 6.0 (Silicon Genetics, Redwood City, CA) for further analysis. Data normalization was performed to scale the data so that the average intensity value on each array was 1 by dividing each expression value by the median of the expression levels on each chip. The individual gene expression levels for each of the 4 pituitary adenoma subtype arrays was divided by the expression level in the normal pituitary array. Thus, the data are presented as relative to the expression in normal pituitary tissue. Filtering was then performed to identify genes over-expressed or under-expressed at least 2.0 fold in tumours compared to normal pituitary. TABLE 1:; The genes / ESTs differentially overexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given. TABLE 2:; The genes / ESTs differentially underexpressed >= 2-fold in at least one pituitary adenoma subtype compared to normal pituitary. Negative values represent underexpression. Where genes are represented by more than one probe set, individual probe data sets are given.
2008-06-12 | E-GEOD-2175 | biostudies-arrayexpress
Project description:mdn1-1 is a weak mutant of the Arabidopsis MDN1 gene. mdn1-1 exhibits an increased seed-size phenotype. To further figure out the proteome changes in the mdn1-1 seeds, we applied the iTRAQ-based LC-MS/MS analysis. A total of 3217 proteins were identified in dry seeds of both wild type and mdn1-1. The significantly differentially expressed proteins (DEPs) were filtered out based on the fold change (FC > 1.5 or < 0.667) of expression level and P value (< 0.05). We defined 282 up- and 40 downregulated proteins in mdn1-1.
2019-11-12 | PXD013784 | Pride
Project description:Analysis of uPA regulation of 4T1 tumor growth at gene expression level. The hypothesis tested in the present study was that overexpression of uPA in 4T1 tumor influence the tumor growth and metastasis related cell signaling pathway. 4T1 cells were stably transduced by lentiviral vector expressing mouse uPA or empty vector. After day 7 of inoculation of 4T1 cells into fatpad of Balb/c mice. Total RNA was extracted from tumor samples (triplicate) using Qiagen total RNA isolation kit. The Illumina MouseWG-6 v2.0 Expression BeadChip (Illumina, Wallingford, CT) was used for gene expression. The raw data from the fluorescence intensity measurements of each array experiment was processed using GeneSpringGX v.11.0 software (Agilent, Santa Clara, CA). Statistical analysis, fold change calculations, and hierarchical clustering of the data were also performed in GeneSpring software. Genes that expressed significantly differently with more than 1.5-fold change and a p-value of <0.05 with respect to controls were taken into consideration. Gene expression data were further validated by qRT-PCR analysis. Pathway analysis was performed by MetaCore software (GeneGo, Inc, St. Joseph, MI).
2012-05-30 | E-GEOD-38346 | biostudies-arrayexpress
Project description:The objective of this study is to identify genes involved in the pathogenesis of multiple myeloma (MM). In this study, we performed gene expression profiling of a total of 29 samples consisting of 19 clinical specimens, 7 myeloma cell lines and 3 normal samples. The microarray data were analysed by GeneSpring Software v14.9. A total of 5124 DEGs were identified in MM group vs control group (fold change≥2.0; P<0.05). Out of 5124 DEGs, 2696 and 2428 genes were up-regulated and down-regulated, respectively in MM group compared to the normal control group.
2022-07-20 | GSE72213 | GEO