scholarly journals Meta-analysis of differentially expressed genes in autism based on gene expression data

2015 ◽  
Vol 14 (1) ◽  
pp. 2146-2155 ◽  
Author(s):  
L.F. Ning ◽  
Y.Q. Yu ◽  
E.T. GuoJi ◽  
C.G. Kou ◽  
Y.H. Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Expression Data

scholarly journals Generation of patterns from gene expression data by assigning confidence to differentially expressed genes

2000 ◽  
Vol 16 (8) ◽  
pp. 685-698 ◽  
Author(s):  
E. Manduchi ◽  
G. R. Grant ◽  
S. E. McKenzie ◽  
G. C. Overton ◽  
S. Surrey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Differentially Expressed ◽  
Expression Data

Association Between Gene Expression Profiles and Commonly Mutated Genes In The Hematopoietic Stem Cells Of Patients With Myelodysplastic Syndromes

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 2779-2779 ◽  
Author(s):  
Andrea Pellagatti ◽  
Moritz Gerstung ◽  
Elli Papaemmanuil ◽  
Luca Malcovati ◽  
Aristoteles Giagounidis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Mutations ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Expression Data ◽  
Common Gene ◽  
Mutational Status

Abstract A particular profile of gene expression can reflect an underlying molecular abnormality in malignancy. Distinct gene expression profiles and deregulated gene pathways can be driven by specific gene mutations and may shed light on the biology of the disease and lead to the identification of new therapeutic targets. We selected 143 cases from our large-scale gene expression profiling (GEP) dataset on bone marrow CD34+ cells from patients with myelodysplastic syndromes (MDS), for which matching genotyping data were obtained using next-generation sequencing of a comprehensive list of 111 genes involved in myeloid malignancies (including the spliceosomal genes SF3B1, SRSF2, U2AF1 and ZRSR2, as well as TET2, ASXL1and many other). The GEP data were then correlated with the mutational status to identify significantly differentially expressed genes associated with each of the most common gene mutations found in MDS. The expression levels of the mutated genes analyzed were generally lower in patients carrying a mutation than in patients wild-type for that gene (e.g. SF3B1, ASXL1 and TP53), with the exception of RUNX1 for which patients carrying a mutation showed higher expression levels than patients without mutation. Principal components analysis showed that the main directions of gene expression changes (principal components) tend to coincide with some of the common gene mutations, including SF3B1, SRSF2 and TP53. SF3B1 and STAG2 were the mutated genes showing the highest number of associated significantly differentially expressed genes, including ABCB7 as differentially expressed in association with SF3B1 mutation and SULT2A1 in association with STAG2 mutation. We found distinct differentially expressed genes associated with the four most common splicing gene mutations (SF3B1, SRSF2, U2AF1 and ZRSR2) in MDS, suggesting that different phenotypes associated with these mutations may be driven by different effects on gene expression and that the target gene may be different. We have also evaluated the prognostic impact of the GEP data in comparison with that of the genotype data and importantly we have found a larger contribution of gene expression data in predicting progression free survival compared to mutation-based multivariate survival models. In summary, this analysis correlating gene expression data with genotype data has revealed that the mutational status shapes the gene expression landscape. We have identified deregulated genes associated with the most common gene mutations in MDS and found that the prognostic power of gene expression data is greater than the prognostic power provided by mutation data. AP and MG contributed equally to this work. JB and PJC are co-senior authors. Disclosures: No relevant conflicts of interest to declare.

Application of a Deep Matrix Factorization Model on Integrated Gene Expression Data

2020 ◽  
Vol 15 (4) ◽  
pp. 359-367
Author(s):  
Yong-Jing Hao ◽  
Mi-Xiao Hou ◽  
Ying-Lian Gao ◽  
Jin-Xing Liu ◽  
Xiang-Zhen Kong
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Matrix Factorization ◽  
Coefficient Matrix ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Complex Data ◽  
Expression Data ◽  
Factorization Model

Background: Non-negative Matrix Factorization (NMF) has been extensively used in gene expression data. However, most NMF-based methods have single-layer structures, which may achieve poor performance for complex data. Deep learning, with its carefully designed hierarchical structure, has shown significant advantages in learning data features. Objective: In bioinformatics, on the one hand, to discover differentially expressed genes in gene expression data; on the other hand, to obtain higher sample clustering results. It can provide the reference value for the prevention and treatment of cancer. Method: In this paper, we apply a deep NMF method called Deep Semi-NMF on the integrated gene expression data. In each layer, the coefficient matrix is directly decomposed into the basic and coefficient matrix of the next layer. We apply this factorization model on The Cancer Genome Atlas (TCGA) genomic data. Results: The experimental results demonstrate the superiority of Deep Semi-NMF method in identifying differentially expressed genes and clustering samples. Conclusion: The Deep Semi-NMF model decomposes a matrix into multiple matrices and multiplies them to form a matrix. It can also improve the clustering performance of samples while digging out more accurate key genes for disease treatment.

IDEG6: a web tool for detection of differentially expressed genes in multiple tag sampling experiments

2003 ◽  
Vol 12 (2) ◽  
pp. 159-162 ◽  
Author(s):  
Chiara Romualdi ◽  
Stefania Bortoluzzi ◽  
Fabio d’Alessi ◽  
Gian Antonio Danieli
Keyword(s):  
Gene Expression ◽  
Statistical Analysis ◽  
Differentially Expressed Genes ◽  
Gene Expression Data ◽  
Differentially Expressed ◽  
Expression Data ◽  
Test Statistics ◽  
Web Tool ◽  
Additional Information

Here we present a novel web tool for the statistical analysis of gene expression data in multiple tag sampling experiments. Differentially expressed genes are detected by using six different test statistics. Result tables, linked to the GenBank, UniGene, or LocusLink database, can be browsed or searched in different ways. Software is freely available at the site: http://telethon.bio.unipd.it/bioinfo/IDEG6_form/ , together with additional information on statistical methodologies.

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