China
Africa
Explore chapters and articles related to this topic
Omics and female reproduction
Published in Moshe Hod, Vincenzo Berghella, Mary E. D'Alton, Gian Carlo Di Renzo, Eduard Gratacós, Vassilios Fanos, New Technologies and Perinatal Medicine, 2019
Morphological assessment of the zona pellucida (ZP) is the gold standard method in oocyte selection in most ART clinics, but this approach is limited by precision boundaries. Studies have shown that differentially expressed candidate genes were respectively overexpressed and underexpressed in cumulus/granulosa cells from oocytes that led to a successful pregnancy, versus oocytes that did not (20,21). ZP properties’ variation is associated with differences in cumulus/granulosa cell gene expression, but ZP morphology is associated with a transcriptomic gene pattern that is not directly related to known gene biomarkers of oocyte development. Further studies using larger lists of candidate markers are required to identify suitable genes highly correlated with ZP morphological criteria, in order to reinforce the accuracy of oocyte selection and potentially increase ART success rates (19).
Forensic Bioinformatics
Published in John Crowley, Antje Hoering, Handbook of Statisticsin Clinical Oncology, 2012
Keith A. Baggerly, Kevin R. Coombes
Another general lesson that we have learned is that clarity and reproducibility are vitally important in high-throughput biology. In keeping with this observation, we instituted the requirement in our department that all analysis reports are written in Sweave [20], a literate programming language that combines R and LaTeX in a way that lets others run your reports and get the same numbers you did. (While we like Sweave, we note that other tools such as GenePattern [21] and Galaxy [22] are available; reproducibility is the goal, and Sweave is a tool for reaching it.) To enhance clarity, we have also imposed a regular structure on our reports, the most important of which is that our reports begin with a short (one or two page) “executive summary” organized in the “Introduction, Data and Methods, Results, Conclusions” style of presentation most familiar to our collaborators. Examples of such reports are available at http://bioinformatics.mdanderson.org/Supplements/ReproRsch-All/.
Immunological and classical subtypes of oral premalignant lesions
Published in OncoImmunology, 2018
Jean-Philippe Foy, Chloé Bertolus, Sandra Ortiz-Cuaran, Marie-Alexandra Albaret, William N Williams, Wenhua Lang, Solène Destandau, Geneviève De Souza, Emilie Sohier, Janice Kielbassa, Emilie Thomas, Sophie Deneuve, Patrick Goudot, Alain Puisieux, Alain Viari, Li Mao, Christophe Caux, SM Lippman, P Saintigny
To characterize the OPL subtypes, we performed an enrichment pathway analysis based on large-scale gene expression data in the four OPL datasets. Pathway-specific pathways were downloaded from the Molecular Signature Database (MSigDB database Molecular Signatures Database v5.2, 2016) and included a total of 1,329 canonical pathways. The ssGSEA was used to compute an enrichment score for each pathway in each sample24,25 which was run from GenePattern.26 Unlike GSEA which analyzes differential pathways between two phenotypical groups, the ssGSEA tool allows for computing an enrichment score (ES) of a given gene set in each sample. The gene expression values for a given sample are rank-normalized, and an ES is produced using the empirical cumulative distribution functions of the genes in the gene set and the remaining genes.24,25 When UP and DN versions of a gene set are available, a combined score was computed. Default parameters were used (weighting component of 0.75 and minimal gene set size of 10 genes).
Integrative analysis identifies an immune-relevant epigenetic signature for prognostication of non-G-CIMP glioblastomas
Published in OncoImmunology, 2021
Anan Yin, Zhende Shang, Amandine Etcheverry, Yalong He, Marc Aubry, Nan Lu, Yuhe Liu, Jean Mosser, Wei Lin, Xiang Zhang, Yu Dong
Differentially expressed microRNAs (DEmiRs) were computed by two-sample standard t test with confidence level of false discovery rate (FDR) assessment = 80% and maximum allowed proportion of false-positive genes = 10%, within BRB-Array Tools (https://brb.nci.nih.gov/BRB-ArrayTools). Somatic mutation data were analyzed by MutSigCV module on GenePattern (https://genepattern.broadinstitute.org/gp) with FDR q-value ≤ 0.05 for significance.18 Segmented copy number data were analyzed by GISTIC2.0 module on GenePattern with default parameters.19MGMT promoter methylation status was determined using a logistic regression model based on two Illumina array probes, i.e., cg12434587 and cg12981137.20 The gene expression subtypes were predicted by Binary tree classification prediction using the 840 classifiers reported by Verhaak et al.3 Gene set enrichment analysis (GSEA) was run to evaluate functional profiles between grouped samples using the gene sets of the Gene Ontology Biological Processes from Molecular Signature Database (MSigDB),21 with both nominal p-values ≤ 0.05 and false discovery rate (FDR) q-values ≤ 0.25 for significance. Single-sample (ss)GSEA was also performed to calculate a separate enrichment score for each pairing of a sample and gene set, which represents the degree to which the genes in a particular gene set are coordinately up- or down-regulated within a sample.21 The abundance of tumor-infiltrating immune cells was estimated by CIBERSORTx (https://cibersortx.stanford.edu/) based on gene expression microarray data of tumor samples.22
Identification of stemness subtypes and features to improve endometrial cancer treatment using machine learning
Published in Artificial Cells, Nanomedicine, and Biotechnology, 2023
Xiaoqin Lu, Yanqi Ying, Wenyi Zhang, Rui Li, Wuliang Wang
The ggpubr package was used to illustrate the differences in TMB between the different stemness subtypes. Then we grouped according to their stemness subtypes and used maftools to identify their gene mutations. TCGAbiolinks package was used to download the CNV data for endometrial cancer. The GenePattern (https://cloud.genepattern.org/gp/pages/index.jsf) tool in the Genomic Identification of Significant Targets in Cancer (GISTIC) software was used for analysis, and the results were visualised using the maftools package. We grouped the results by stemness subtype to show the different mutations in biomarkers (PTEN, TP53, KRAS, FGFR2, APC, MUC16, and CEACAM5).