Project description:Gene expression profile of human chordoma samples. These samples were combined with the chordoma samples from those of E-MEXP-353. These were normalised and batch corrected as per materials and methods in the published paper. Gene set enrichment analysis was then performed to determine enrichment of target genes of T between the rs2305089 genotypes (GA vs AA).
Project description:Ascertain the effects of disease-causing gene mutations on the differentiation status of human naïve CD4+ T cells in the setting of primary immunodeficiencies. Thus, do CD4+ T cells isolated according to a naïve surface phenotype (ie CD4+CD45RA+CCR7+) from healthy donors exhibit a similar gene expression profile as phenotpyically-matched cells isolated from individuals with defined primary immunodeficiencies caused by specific monogenic mutations. Naïve CD4+ T cells were isolated from PBMCs of healthy controls or from different PID patients by sort-purifying CD4+CD45RA+CD127+CD25lo cells. Memory CD4+ T cells from healthy controls were isolated as CD4+CD45RA- cells. RNA was extracted, transcribed to cDNA and then gene expression analysis determined using Affymetrix Human Gene 2.0 or Human Gene 2.1 ST microarray chips. Normalisation (robust multichip average), log2 transformation and probe set summarisation were performed for each dataset using Bioconductor packages implemented in the R statistical computing environment, version 3.1.1. Subsequent processing and analyses were carried out using GenePattern modules (available at http://pwbc.garvan.unsw.edu.au/gp/). The datasets were merged, quantile normalised, and batch corrected using the MergeColumns, NormaliseColumns and ComBat modules. Differential gene expression analysis was assessed using LimmaGP. The top 100 differentially expressed genes between normal healthy naïve and normal healthy memory T cells, as determined by LimmaGP analysis, were used to filter the combined and batch corrected dataset of naïve CD4+ T cells from 7 healthy controls and 19 PID patients, and memory CD4+ T cells from 3 healthy controls. Unsupervised clustering was performed using NMFConsensus and NMF (Brunet et al., 2004).
Project description:Breast cancer brain metastasis is a rising occurrence, necessitating a better understanding of the mechanisms involved for effective management. Breast cancer brain metastases diverge significantly from the primary tumor, with gains in kinase and concomitant losses of steroid signaling observed. In this study, we explored the role of the kinase receptor RET in promoting breast cancer brain metastasis and provide a rationale for targeting the receptor in this patient cohort. Here, exome capture RNA sequencing data is deposited as sequencing batch corrected log2 transformed trimmed M of means (TMM) normalised counts per million (CPM) (log2(TMM-CPM +1) gene expression values for the patients described in this study (n=19,622 protein coding genes; N=16 tumour samples).
Project description:The spontaneous mutant Bronx waltzer (bv) mouse line is characterized by deafness and balance defect. We located the bv mutation to the Srrm4 gene which encodes a regulator of alternative pre-mRNA splicing. We found that Srrm4 is expressed in balance and hearing organs (i.e. in the vestibular maculas and the cochlea). Srrm4 is also expressed in the central nervous system including the cerebellum. To identify potential splicing defects in bv/bv mice, we analyzed RNA samples from the vestibular maculas and cerebellums of bv/bv mice and control (bv/+) littermates, using mouse exon junction microarrays (MJAY). In this dataset, we include probe-set level data obtained from vestibular macula samples. The processed data represent probe-set intensities that have been normalized to gene expression levels (Inorm). Inorm was calculated using batch-corrected data as well as data that were not corrected for a batch effect.
Project description:This data is part of a pre-publication release. Here, we present a meta-dataset exclusively comprising of 1,118 samples including primary non-small cell lung cancer (NSCLC) tumors and normal lung tissues from ten independent GEO datasets. The meta-dataset has been merged, normalized, batch effect corrected and filtered for genes with low variance using our developed bioinformatics pipeline utilising multiple open source R packages. This meta-dataset serves as an accurate and powerful 'discovery cohort' for clinical model development.
Project description:In this dataset, we include the expression data obtained from dissected mouse 16.5 embryonic brains using 3 wild type and 3 Tdp2Delta1-3 individuals. These data are used to obtain 165 genes that are differentially expressed as a consequence of Tdp2 absence. The data (obtained from three independent pairs of wild type and TDP2D1-3 brains) was normalised using RMA (Robust Multi-Array Average) and genes changing 1.5 fold (corrected p-value cut-off:0.05) between Tdp2+/+ and Tdp2Delta1-3
Project description:We present a meta-dataset comprising of a total of 178 samples including both primary tumors and tumor-free pancreatic tissues from four independent GEO datasets. To minimise inter-platform variation, only datasets generated from the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) were processed to develop the meta-dataset. Using multiple open source R packages implemented in our previously developed bioinformatics pipeline, each dataset has been preprocessed with RMA normalisation, merged, and batch effect-corrected via Combat method. With increased sample size, the present meta-dataset serves an excellent 'discovery cohort' for discovering differentially expressed in diseased phenotype.
Project description:We present a meta-dataset comprising of a total of 663 samples including both primary tumors and tumor-free ovarian tissues from ten independent GEO datasets. To minimise inter-platform variation, only datasets generated from the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) were processed to develop the meta-dataset. Using multiple open source R packages implemented in our previously developed bioinformatics pipeline, each dataset has been preprocessed with RMA normalisation, merged, and batch effect-corrected via Combat method. With increased sample size, the present meta-dataset serves an excellent 'discovery cohort' for discovering differentially expressed in diseased phenotype.
Project description:We present a meta-dataset comprising of a total of 347 samples including both primary tumors and tumor-free renal tissues from six independent GEO datasets. To minimise inter-platform variation, only datasets generated from the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) were processed to develop the meta-dataset. Using multiple open source R packages implemented in our previously developed bioinformatics pipeline, each dataset has been preprocessed with RMA normalisation, merged, and batch effect-corrected via Combat method. With increased sample size, the present meta-dataset serves an excellent 'discovery cohort' for discovering differentially expressed in diseased phenotype.
Project description:We present a meta-dataset comprising of a total of 237 samples including both primary tumors and tumor-free prostate tissues from six independent GEO datasets. To minimise inter-platform variation, only datasets generated from the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array) were processed to develop the meta-dataset. Using multiple open source R packages implemented in our previously developed bioinformatics pipeline, each dataset has been preprocessed with RMA normalisation, merged, and batch effect-corrected via Combat method. With increased sample size, the present meta-dataset serves an excellent 'discovery cohort' for discovering differentially expressed in diseased phenotype.