NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis

2018 ◽  
Vol 18 (2) ◽  
pp. 732-740 ◽  
Author(s):  
Jakob Willforss ◽  
Aakash Chawade ◽  
Fredrik Levander
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
High Sensitivity ◽  
Expression Data ◽  
Online Tool

scholarly journals Signatures and Prognostic Values of Related Immune Targets in Tongue Cancer

2021 ◽  
Author(s):  
Xi Yu ◽  
Xiaofei Lv
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Tongue Cancer ◽  
Differential Expression Analysis ◽  
Marker Genes ◽  
Data Sets ◽  
Expression Data ◽  
Oral Cancers ◽  
Limma Package ◽  
Cancer Bioinformatics

Abstract Tongue cancer, as one of the most malignant oral cancers, is highly invasive and has a high risk of recurrence. At present, tongue cancer in the advanced stage is not obvious, easy to miss the opportunity of early diagnosis. It is important to find markers that can predict the occurrence and progression of tongue cancer. Bioinformatics analysis plays an important role in the acquisition of marker genes. GEO and TCGA data are very important public databases. In addition to expression data, TCGA database also contains corresponding clinical data. In this study, we screened three GEO datasets included GSE13601, GSE34105 and GSE34106 that met the standard. These data sets were combined using the SVA package to prepare the data for differential expression analysis, and then the LIMMA package was used to set the standard to p<0.05 and |log2 (FC)| ≥1.5. We got 170 DEGs (104, raised 66 downgrade). Besides, the DEseq package was used for differential expression analysis using the same criteria for samples in TCGA database. It ended up with 1589 DEGs (644 up-regulated, 945 down-regulated). By merging these two sets of DEGs, 5 common up-regulated DEGs (CCL20, SCG5, SPP1, KRT75 and FOLR3) and 15 common down-regulated DEGs were obtained. Further functional analysis of the DEGs showed that CCL20, SCG5 and SPP1 is closely related to prognosis and may be a therapeutic target of TSCC.

scholarly journals DECODE: an integrated differential co-expression and differential expression analysis of gene expression data

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Thomas WH Lui ◽  
Nancy BY Tsui ◽  
Lawrence WC Chan ◽  
Cesar SC Wong ◽  
Parco MF Siu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Expression Data

scholarly journals Hybrid analysis of gene dynamics predicts context-specific expression and offers regulatory insights

2019 ◽  
Vol 35 (22) ◽  
pp. 4671-4678
Author(s):  
Justin D Finkle ◽  
Neda Bagheri
Keyword(s):  
Gene Expression ◽  
Differential Equation ◽  
Time Series ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Supplementary Information ◽  
Series Data ◽  
Expression Data ◽  
Regulatory Pathways

Abstract Motivation To understand the regulatory pathways underlying diseases, studies often investigate the differential gene expression between genetically or chemically differing cell populations. Differential expression analysis identifies global changes in transcription and enables the inference of functional roles of applied perturbations. This approach has transformed the discovery of genetic drivers of disease and possible therapies. However, differential expression analysis does not provide quantitative predictions of gene expression in untested conditions. We present a hybrid approach, termed Differential Expression in Python (DiffExPy), that uniquely combines discrete, differential expression analysis with in silico differential equation simulations to yield accurate, quantitative predictions of gene expression from time-series data. Results To demonstrate the distinct insight provided by DiffExpy, we applied it to published, in vitro, time-series RNA-seq data from several genetic PI3K/PTEN variants of MCF10a cells stimulated with epidermal growth factor. DiffExPy proposed ensembles of several minimal differential equation systems for each differentially expressed gene. These systems provide quantitative models of expression for several previously uncharacterized genes and uncover new regulation by the PI3K/PTEN pathways. We validated model predictions on expression data from conditions that were not used for model training. Our discrete, differential expression analysis also identified SUZ12 and FOXA1 as possible regulators of specific groups of genes that exhibit late changes in expression. Our work reveals how DiffExPy generates quantitatively predictive models with testable, biological hypotheses from time-series expression data. Availability and implementation DiffExPy is available on GitHub (https://github.com/bagherilab/diffexpy). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals Development of Poly(A)-ClickSeq as a tool enabling simultaneous genome-wide poly(A)-site identification and differential expression analysis

Methods ◽  
2019 ◽  
Vol 155 ◽  
pp. 20-29 ◽  
Author(s):  
Nathan D. Elrod ◽  
Elizabeth A. Jaworski ◽  
Ping Ji ◽  
Eric J. Wagner ◽  
Andrew Routh
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Genome Wide ◽  
A Site ◽  
Site Identification
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