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Cancer Informatics
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
So far, we have looked at one gene at a time in our analyses. Gene Set Enrichment Analysis (GSEA) looks at sets of genes at a time. The idea is to see if a set of genes associated with a particular gene pathway, or biological function, or process are well represented in the over or under expressed genes that have been identified in the samples. We do not go into the details of how this in done.
Multi-omics Analysis
Published in Altuna Akalin, Computational Genomics with R, 2020
The recent decades of genomics have uncovered many of the ways in which genes cooperate to perform biological functions in concert. This work has resulted in rich annotations of genes, groups of genes, and the different functions they carry out. Examples of such annotations include the Gene Ontology Consortium’s GO terms (Ashburner et al., 2000, Consortium (2017)), the Reactome pathways database (Fabregat et al., 2018 b), and the Kyoto Encyclopaedia of Genes and Genomes (Kanehisa et al., 2017). These resources, as well as others, publish lists of so-called gene sets, or pathways, which are sets of genes which are known to operate together in some biological function, e.g. protein synthesis, DNA mismatch repair, cellular adhesion, and many other functions. Gene set enrichment analysis is a method which looks for overlaps between genes which we have found to be of interest, e.g. by them being implicated in a certain tumor type, and the a-priori gene sets discussed above.
Pharmacogenetics of Breast Cancer: Toward the Individualization of Therapy
Published in Brian Leyland-Jones, Pharmacogenetics of Breast Cancer, 2020
Jenny C. Chang, Susan G. Hilsenbeck, Suzanne A. W. Fuqua
In vitro drug sensitivity data in NCI60 cell lines sensitive and resistant to specific chemotherapeutic agents (adriamycin, cyclophosphamide, docetaxel, etoposide, 5-fluoruracil, paclitaxel, topotecan) were recently published (16). Gene set enrichment analysis was performed, and gene expression signatures predictive of sensitivity to individual chemotherapeutic drugs were reported. Each signature was validated with response data from an independent set of cell line studies. These signatures were then used to predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, were found to also predict response to multidrug regimens (16). These profiles are being validated in prospective clinical trials.
Network-based survival analysis to discover target genes for developing cancer immunotherapies and predicting patient survival
Published in Journal of Applied Statistics, 2021
Xinwei He, Xiaoqiang Sun, Yongzhao Shao
As is well known, a gene typically does not function alone to cause disease progression. Instead, genes interact with each other and work together in pathways. Functionally important pathways can be obtained from gene set enrichment analysis. Thus, the candidate gene set identified in functionally important gene clusters tends to be reproducible. Also, for the purpose of effective therapeutic intervention, target genes that derived from gene clusters are more likely to contain driver genes instead of passenger genes in the disease progression. In this study, we construct a weighted gene correlation network after translating the findings in animal models to those in humans, and we group the highly correlated genes into clusters within the network. Pearson correlation coefficient is used to encode the correlation between the expression of genes in the network. Subsequently, target genes are selected from the clusters which are enriched in important biological pathways and are highly associated with patient survival. The identified target genes could serve as the predictors of patient survival, as well as new targets for developing immunotherapies, which may improve the treatment effectiveness for those patients who do not have a durable response to the current therapeutic strategy.
Single-cell RNA sequencing: An overview for the ophthalmologist
Published in Seminars in Ophthalmology, 2021
Elizabeth J. Rossin, Lucia Sobrin, Leo A. Kim
After quality control and normalization, one typically performs dimensionality reduction [principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and Gaussian process latent variable modeling] to transform the data from 20,000 variables (one for each gene) to a set of vectors that explain most of the variability. The first couple principal components are usually technical factors and some argue to remove them.31 Finally, the normalized data are clustered along the principal components of variation, and these clusters are presumed to represent different groups of transcriptionally similar cell types (Figure 1C). Various approaches have been employed to identify which genes or pathways are driving the difference between the groups of cells such as gene set enrichment analysis. The distribution of transcript quantities usually follows a negative binomial distribution or a multimodal distribution (if the population of cells is heterogeneous) and this requires careful non-parametric consideration.10
Complement inhibitor factor H expressed by breast cancer cells differentiates CD14+ human monocytes into immunosuppressive macrophages
Published in OncoImmunology, 2020
Karolina I. Smolag, Christine M. Mueni, Karin Leandersson, Karin Jirström, Catharina Hagerling, Matthias Mörgelin, Paul N. Barlow, Myriam Martin, Anna M. Blom
RNA from monocytes of six donors incubated for 48 h with or without FH had an RNA quality indicator of at least 8.3 (Experion RNA chip; Bio-Rad). RNA was hybridized with the Affymetrix Clariom D chip. Data were normalized using the Robust MultiChip Averaging algorithm.25 All arrays were quality controlled by the visual inspection of MDS- and MA-plots. To identify differentially expressed genes, a linear model was fitted, using donor as a blocking factor and treatment as the main outcome. To adjust for multiple testing the Benjamini and Hochberg method was applied26 and q-values <0.05 were considered significant with no cutoff for fold change. For gene-set analysis, we applied the Generally Applicable Gene-set Enrichment methodology.27 All microarray data and metadata are available under accession number GSE129670 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129670).