scholarly journals Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed

Biostatistics ◽  
2015 ◽  
Vol 17 (1) ◽  
pp. 16-28 ◽  
Author(s):  
Laurent Jacob ◽  
Johann A. Gagnon-Bartsch ◽  
Terence P. Speed

Abstract When dealing with large scale gene expression studies, observations are commonly contaminated by sources of unwanted variation such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g. when the goal is to cluster the samples or to build a corrected version of the dataset—as opposed to the study of an observed factor of interest—taking unwanted variation into account can become a difficult task. The factors driving unwanted variation may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. We show how negative control genes and replicate samples can be used to estimate unwanted variation in gene expression, and discuss how this information can be used to correct the expression data. The proposed methods are then evaluated on synthetic data and three gene expression datasets. They generally manage to remove unwanted variation without losing the signal of interest and compare favorably to state-of-the-art corrections. All proposed methods are implemented in the bioconductor package RUVnormalize.

2008 ◽  
Vol 68 (2) ◽  
pp. 447-452 ◽  
Author(s):  
CA. Sommer ◽  
F. Henrique-Silva

Even though the molecular mechanisms underlying the Down syndrome (DS) phenotypes remain obscure, the characterization of the genes and conserved non-genic sequences of HSA21 together with large-scale gene expression studies in DS tissues are enhancing our understanding of this complex disorder. Also, mouse models of DS provide invaluable tools to correlate genes or chromosome segments to specific phenotypes. Here we discuss the possible contribution of HSA21 genes to DS and data from global gene expression studies of trisomic samples.


Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 2779-2779
Author(s):  
Naomi Galili ◽  
Pablo Tamayo ◽  
Olga B Botvinnik ◽  
Jill P Mesirov ◽  
Jennifer Zikria ◽  
...  

Abstract Abstract 2779 Interpretation of gene expression studies in MDS have been especially challenging due to the heterogeneity of the cell lineages that comprise the malignant clone. In attempting to overcome these difficulties we have used a bedside-to-bench approach to define an expression signature that may identify patients likely to respond. Ezatiostat hydrochloride (TLK199) is an inhibitor of glutathione S-transferase, an enzyme that is over expressed in many cancers, and has been shown in vitro to stimulate growth and differentiation of hematopoietic progenitor cells and to induce apoptosis in leukemia cells. Based on multilineage responses in low-Int1 MDS patients in our phase 2 study of oral TLK199, a multi institutional phase 2 study was conducted in low-Int1 patients. Response was evaluated by International Working Group (IWG 2006) criteria. Pre-therapy bone marrow mononuclear cells of patients treated with TLK199 were analyzed for gene expression on the Illumina HT12v4 whole genome array with IRB approval. RNA isolated from the marrow mononuclear cells was available on 9 responders (R) and 21 non-responders (NR). Five R and 13 NR were randomly chosen to create a training set with the intent to later use the remaining samples for model testing. We identified the top 100 differentially expressed genes using a sensitive metric based on the normalized mutual information. We also performed single-sample Gene Set Enrichment Analysis to find the most salient differences in terms of pathways and biological processes between R/NR. Of special note are the 4 microRNA s differentially expressed between R/NR. Three miRNAs are under-expressed (miR-129, 802 and 548e) and one (miR-155) is over-expressed in R. Reduced expression of miR-129 has been reported in solid tumors when over-expressed has been shown to have anti-proliferative activity in cell lines. SOX4 is a target gene for miR129 and reduced expression of miR-129 results in concomitant up-regulation of SOX4 mRNA which can function as both an oncogene and a tumor suppressor gene depending on tumor lineage. Over-expression of SOX4 inhibited cytokine induced granulocyte maturation in the myeloid 32Dcl3 cell line suggesting a possible role in MDS. MiR-802 targets the receptor for angiotensin II and when expression is decreased there is increased angiotensin II activity. It has recently been shown that angiotensin is a pro-inflammatory mediator that participates in apoptosis, angiogenesis and promotes mitochondrial dysfunction, all characteristics of MDS. In addition, the transcription factor ZFHX3, a predicted target of miR-802, is a negative regulator of c-MYB which has been shown to be up-regulated in all subtypes of MDS. Similarly, c-MYB is a predicted target of miR-155, which is over-expressed in TLK199 responders. MiR-155 was shown to be over-expressed in marrow cells of a subset of human AML patients. Of particular note are the studies showing that sustained expression of miR-155 in mouse hematopoietic stem cells cause a myeloproliferative/myelodysplastic disorder. Subsequent pathway analysis of this expression data revealed that a JNK gene set as defined from the GEO dataset GDSS8081 was consistently under-expressed in responders and over-expressed in non-responders. TLK199 has been shown to induce JUN/JNK by binding to glutathione S-transferase, a key inhibitor of this pathway. The expression data confirms that patients whose pre-therapy marrow shows under-expression of the JNK gene set are precisely those who benefit from this drug therapy and those patients who already over-express these genes are unlikely to respond. This study highlights two important points: 1) Using a bedside-to-bench strategy yielded a signature that distinguished responders from non-responders 2) The signature identified genes and signaling pathways that shed light on both the biology of the disease and the mechanism of action of the drug. In conclusion, if these results are confirmed in the test set, we will use the signature in a future prospective study to preselect MDS patients for therapy with this promising drug. Disclosures: Brown: Telik, Inc.: Employment, Equity Ownership.


2019 ◽  
Vol 16 (3) ◽  
Author(s):  
Nimisha Asati ◽  
Abhinav Mishra ◽  
Ankita Shukla ◽  
Tiratha Raj Singh

AbstractGene expression studies revealed a large degree of variability in gene expression patterns particularly in tissues even in genetically identical individuals. It helps to reveal the components majorly fluctuating during the disease condition. With the advent of gene expression studies many microarray studies have been conducted in prostate cancer, but the results have varied across different studies. To better understand the genetic and biological regulatory mechanisms of prostate cancer, we conducted a meta-analysis of three major pathways i.e. androgen receptor (AR), mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) on prostate cancer. Meta-analysis has been performed for the gene expression data for the human species that are exposed to prostate cancer. Twelve datasets comprising AR, mTOR, and MAPK pathways were taken for analysis, out of which thirteen potential biomarkers were identified through meta-analysis. These findings were compiled based upon the quantitative data analysis by using different tools. Also, various interconnections were found amongst the pathways in study. Our study suggests that the microarray analysis of the gene expression data and their pathway level connections allows detection of the potential predictors that can prove to be putative therapeutic targets with biological and functional significance in progression of prostate cancer.


2017 ◽  
Author(s):  
Weiguang Mao ◽  
Elena Zaslavsky ◽  
Boris M. Hartmann ◽  
Stuart C. Sealfon ◽  
Maria Chikina

AbstractA major challenge in gene expression analysis is to accurately infer relevant biological insight, such as regulation of cell type proportion or pathways, from global gene expression studies. We present a general solution for this problem that outperforms available cell proportion inference algorithms, and is more widely useful to automatically identify specific pathways that regulate gene expression. Our method improves replicability and biological insight when applied to trans-eQTL identification.


2017 ◽  
Vol 3 (4) ◽  
pp. 186
Author(s):  
Redi Aditama ◽  
Zulfikar Achmad Tanjung ◽  
Widyartini Made Sudania ◽  
Toni Liwang

<p class="Els-Abstract-text">RNA-seq using the Next Generation Sequencing (NGS) approach is a common technology to analyze large-scale RNA transcript data for gene expression studies. However, an appropriate bioinformatics tool is needed to analyze a large amount of transcriptomes data from RNA-seq experiment. The aim of this study was to construct a system that can be easily applied to analyze RNA-seq data. RNA-seq analysis tool as SMART-RDA was constructed in this study. It is a computational workflow based on Galaxy framework to be used for analyzing RNA-seq raw data into gene expression information. This workflow was adapted from a well-known Tuxedo Protocol for RNA-seq analysis with some modifications. Expression value from each transcriptome was quantitatively stated as Fragments Per Kilobase of exon per Million fragments (FPKM). RNA-seq data of sterile and fertile oil palm (Pisifera) pollens derived from Sequence Read Archive (SRA) NCBI were used to test this workflow in local facility Galaxy server. The results showed that differentially gene expression in pollens might be responsible for sterile and fertile characteristics in palm oil Pisifera.</p><p><strong>Keywords:</strong> FPKM; Galaxy workflow; Gene expression; RNA sequencing.</p>


2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Zhonggang Hou ◽  
Peng Jiang ◽  
Scott A. Swanson ◽  
Angela L. Elwell ◽  
Bao Kim S. Nguyen ◽  
...  

2009 ◽  
Vol 2 (1) ◽  
pp. 235 ◽  
Author(s):  
Joëlle Vermeulen ◽  
Stefaan Derveaux ◽  
Steve Lefever ◽  
Els De Smet ◽  
Katleen De Preter ◽  
...  

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