A new normalization for Nanostring nCounter gene expression data

AbstractThe Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology.

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A new normalization for the Nanostring nCounter gene expression assay

10.1101/374173 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ramyar Molania ◽

Johann A. Gagnon-Bartsch ◽

Alexander Dobrovic ◽

Terence P Speed

Keyword(s):

Gene Expression ◽

Single Molecule ◽

Housekeeping Genes ◽

Positive Control ◽

Negative Control ◽

Normalization Method ◽

Data Sets ◽

Analysis Software ◽

Gene Expression Assay ◽

Single Reaction

AbstractThe Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency, and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different data sets. We also offer suggestions on the design and analysis of studies involving this technology.

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ICN: a normalization method for gene expression data considering the over-expression of informative genes

Molecular BioSystems ◽

10.1039/c6mb00386a ◽

2016 ◽

Vol 12 (10) ◽

pp. 3057-3066 ◽

Cited By ~ 13

Author(s):

Lixin Cheng ◽

Xuan Wang ◽

Pak-Kan Wong ◽

Kwan-Yeung Lee ◽

Le Li ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Normalization Method ◽

Expression Data ◽

Over Expression ◽

Global Increase ◽

Microarray Studies

The global increase of gene expression has been frequently established in cancer microarray studies.

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Prostaglandin E2 induces ovulation in prepubertal mice

Brazilian Journal of Veterinary Research and Animal Science ◽

10.11606/issn.1678-4456.bjvras.2021.182745 ◽

2021 ◽

Vol 58 ◽

pp. e182745

Author(s):

Jéssica de Souza Andrade ◽

Juliana Pavan Zuliani ◽

Jaswant Singh ◽

Sulamita da Silva Setúbal ◽

Renata Reis da Silva ◽

...

Keyword(s):

Gene Expression ◽

Prostaglandin E2 ◽

Positive Control ◽

Negative Control ◽

Cox1 Gene ◽

Gene Expressions ◽

Chi Square ◽

Chi Square Test ◽

Phosphate Buffered Saline ◽

Cox2 Expression

The objective of this study was to determine the ability of prostaglandin E2 (PGE2) to induce ovulation and expression of PGE2 receptor (EP2 and EP4) and COX genes (COX-1 and COX-2) in the ovary and pituitary of prepubertal mice. The positive control consisted of the application of 5 μg of gonadotropin-releasing hormone (GnRH, n = 29); the negative control applied 0.5 mL of phosphate buffered saline (PBS, n=31); the treatment tested the application of 250 μg of PGE2 (n = 29), making a total of 89 prepubertal mice (BALB/c). Mice were euthanized 14 to 15 h after treatments to detect ovulation and tissue collection. A Chi-square test was used to compare the proportion of animals ovulating. Gene expressions and number of ovulation were analyzed by one-way ANOVA and Tukey’s test was used to compare means among groups. A greater proportion of mice (P < 0.001) ovulated after receiving GnRH (89.7%, 26/29) compared to PGE2 group (58.6%, 17/29). However, the proportion was higher compared to those treated with PBS (0%, 0/31). Ep2 gene expression in the pituitary was > two-fold higher (P < 0.05) in the PGE2 group compared to the PBS and GnRH groups. Further, PGE2 stimulated Cox1 (2.7 fold, P < 0.05) while GnRH stimulated Cox2 expression (6.5 fold, P < 0.05) in the pituitary when compared to the PBS group. In conclusion, our results support the hypothesis that PGE2 can induce ovulation in prepubertal mice with a concomitant increase in Ep2 and Cox1 gene expression in the pituitary gland.

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GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues

Physiological Genomics ◽

10.1152/physiolgenomics.00025.2005 ◽

2005 ◽

Vol 21 (3) ◽

pp. 389-395 ◽

Cited By ~ 392

Author(s):

Robert D. Barber ◽

Dan W. Harmer ◽

Robert A. Coleman ◽

Brian J. Clark

Keyword(s):

Gene Expression ◽

Mrna Expression ◽

Gene Expression Data ◽

Internal Control ◽

Housekeeping Genes ◽

Housekeeping Gene ◽

Human Tissues ◽

Expression Data ◽

Expression Levels ◽

Gapdh Mrna

Quantitative gene expression data are often normalized to the expression levels of control or so-called “housekeeping” genes. An inherent assumption in the use of housekeeping genes is that expression of the genes remains constant in the cells or tissues under investigation. Although exceptions to this assumption are well documented, housekeeping genes are of value in fully characterized systems. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is one of the most commonly used housekeeping genes used in comparisons of gene expression data. To investigate the value of GAPDH as a housekeeping gene in human tissues, the expression of GAPDH mRNA was measured in a panel of 72 different pathologically normal human tissue types. Measurements were obtained from 371,088 multiplexed, quantitative real-time RT-PCRs with specific target genes. Significant differences in the expression levels of GAPDH mRNA were observed between tissue types and between donors of the same tissue. A 15-fold difference in GAPDH mRNA copy numbers was observed between the highest and lowest expressing tissue types, skeletal muscle and breast, respectively. No specific effect of either age or gender was observed on GAPDH mRNA expression. These data provide an extensive analysis of GAPDH mRNA expression in human tissues and confirm previous reports of the marked variability of GAPDH expression between tissue types. These data establish comparative levels of expression and can be used to add value to gene expression data in which GAPDH is used as the internal control.

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ADAGE analysis of publicly available gene expression data collections illuminates Pseudomonas aeruginosa-host interactions

10.1101/030650 ◽

2015 ◽

Cited By ~ 4

Author(s):

Jie Tan ◽

John H Hammond ◽

Deborah A Hogan ◽

Casey S Greene

Keyword(s):

Gene Expression ◽

Pseudomonas Aeruginosa ◽

Gene Expression Data ◽

Cellular Response ◽

Bacterial Pathogen ◽

Positive Control ◽

Expression Data ◽

Low Oxygen ◽

Biological Interpretation ◽

Data Collections

The growth in genome-scale assays of gene expression for different species in publicly available databases presents new opportunities for computational methods that aid in hypothesis generation and biological interpretation of these data. Here, we present an unsupervised machine-learning approach, ADAGE (Analysis using Denoising Autoencoders of Gene Expression) and apply it to the interpretation of all of the publicly available gene expression data for Pseudomonas aeruginosa, an important opportunistic bacterial pathogen. In post-hoc positive control analyses using curated knowledge, the P. aeruginosa ADAGE model found that co-operonic genes often participated in similar processes and accurately predicted which genes had similar functions. By analyzing newly generated data and previously published microarray and RNA-seq data, the ADAGE model identified gene expression differences between strains, modeled the cellular response to low oxygen, and predicted the involvement of biological processes despite low level expression differences in directly involved genes. Comparison of ADAGE with PCA and ICA revealed that ADAGE extracts distinct signals. We provide the ADAGE model with analysis of all publicly available P. aeruginosa GeneChip experiments, and we provide open source code for use in other species and settings.

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Systematic Normalization with Multiple Housekeeping Genes for the Discovery of Genetic Dependencies in Cancer

10.1101/2020.01.29.925651 ◽

2020 ◽

Cited By ~ 1

Author(s):

Oliver Bonham-Carter ◽

Yee Mon Thu

Keyword(s):

Gene Expression ◽

New York ◽

Gene Expression Data ◽

Genetic Correlations ◽

Housekeeping Genes ◽

Data Sets ◽

List Type ◽

Expression Data ◽

Multiple Gene ◽

Gene Normalization

ABSTRACTCancer results from complex interactions between genes that are misregulated. Although our understanding of the contribution of single genes to cancer is expansive, the interplay between genes in the context of this devastating disease remains to be understood. Using the Genomic Data Commons Data Portal through National Cancer Institute, we randomly selected ten data sets of breast cancer gene expression, acquired by RNA sequencing to be subjected to a computational method for the exploration of genetic interactions at a large scale. We focused on genes that suppress genome instability (GIS genes) since function or expression of these genes is often altered in cancer.In this paper, we show how to discover pairs of genes whose expressions demonstrate patterns of correlation. To ensure an inter-comparison across data sets, we tested statistical normalization approaches derived from the expression of randomly selected single housekeeping genes, or from the average of three. In addition, we systematically selected ten housekeeping genes for the purpose of normalization. Using normalized expression data, we determined R2 values from linear models for all possible pairs of GIS genes and presented our results using heatmaps.Despite the heterogeneity of data, we observed that multiple gene normalization revealed more consistent correlations between pairs of genes, compared to using single gene expressions. We also noted that multiple gene normalization using ten genes outperformed normalization using three randomly selected genes. Since this study uses gene expression data from cancer tissues and begins to address the reproducibility of correlation between two genes, it complements other efforts to identify gene pairs that co-express in cancer cell lines. In the future, we plan to define consistent genetic correlations by using gene expression data derived from different types of cancer and multiple gene normalization.CCS CONCEPTSApplied computing → Computational biology.ACM Reference FormatOliver Bonham-Carter and Yee Mon Thu. 2019. Systematic Normalization with Multiple Housekeeping Genes for the Discovery of Genetic Dependencies in Cancer. In Niagara Falls, New York. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn

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Perubahan Rasio Ekspresi mRNA MMP-8/TIMP-1 Setelah Aplikasi Gel Ekstrak Bunga Rosella 10% pada Pasien dengan Gingivitis Setelah Pemasangan Mahkota Tiruan Akrilik

Jurnal Material Kedokteran Gigi ◽

10.32793/jmkg.v7i2.375 ◽

2018 ◽

Vol 7 (2) ◽

pp. 56

Author(s):

Lenni Indriani ◽

Mohammad Dharmautama ◽

Edy Machmud ◽

Muhammad Natsir Djide ◽

Mochammad Hatta

Keyword(s):

Gene Expression ◽

Acrylic Resin ◽

Negative Control Group ◽

Positive Control ◽

Negative Control ◽

Control Group ◽

Expression Ratio ◽

Polymerase Chain ◽

Herbal Plant ◽

Cervical Area

The acrylic-resin crown is still used as a choice in the manufacture of artificial crown because it is easy in the procedure. However accumulation plaque around cervical of the acrylic-resin crown can lead to the occurrence of gingivitis. Some research found that degradation of ECM by MMP-8 and TIMP-1 as a inhibitor during gingivitis. Roselle as a herbal plant in the treatment began to increase. Roselle is rich in Phytochemical compound which has been shown to reduce inflammation. This research determined effectiveness of roselle extract gel 10% in reducing gingivitis in patients using acrylic-resin crown and related to changes in mRNA MMP-8/TIMP-1 gene expression ratio. The subjects were 9 patients who has gingivitis post resin-acrylic crown insertion and divided in to three groups treatment with roselle extract gel 10%, negative control (base gel), and positive control (Povidon iodine). GCF samples were taken in cervical area of acrylic-resin crown with paper strips before and seven days after application of roselle extract gel 10%. The change of ratio MMP-8 and TIMP-1 m RNA gene expression is tested using Real Time-Polymerase Chain Reaction. Paired- T test shows on negative control group (p=0,301, p<0,05) does not show any change ratio of mRNA MMP- 8/TIMP-1, on positive control, there is changing ratio but not significant(p =0,060). The treatment group shows significant change in ratio of mRNA MMP-8 /TIMP-1 gene expression(p=0,018) Conclusion, Extract Roselle gel 10% can reduce the gingivitis and change the ratio of MMP-8 / TIMP 1 mRNA.

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An integrated approach in gene-expression landscape profiling to identify housekeeping and tissue-specific genes in cattle

Animal Production Science ◽

10.1071/an20689 ◽

2021 ◽

Vol 61 (16) ◽

pp. 1643

Author(s):

Peng Li ◽

Yun Zhu ◽

Xiaolong Kang ◽

Xingang Dan ◽

Yun Ma ◽

...

Keyword(s):

Gene Expression ◽

Expression Analysis ◽

Large Scale ◽

Gene Expression Analysis ◽

Housekeeping Genes ◽

Analysis Tool ◽

Expression Data ◽

Rna Seq ◽

Web Based ◽

Tissue Specific

Context High-throughput transcriptome sequencing (RNA-Seq) has been widely applied in cattle studies. Public databases such as the National Center for Biotechnology Information (NCBI) contain large collections of gene expression data from various cattle tissues that can be used in gene expression analysis research Aims This study was conducted to investigate patterns of transcriptome variation across tissues of cattle through large-scale identification of housekeeping genes (i.e. those crucial to maintaining basic cellular activity) and tissue-specific genes in cattle tissues. Methods Using data available in the NCBI Sequence Read Archive database, we analysed 1377 transcriptome data sequences from 60 bovine tissue types, identified tissue-specific and housekeeping genes, and set up a web-based bovine gene expression analysis tool. Key results We found 101 genes widely expressed in almost all tissue and screened out five housekeeping genes: RPL35A, eIF4A2, GAPDH, IPO5 and PAK2. Focusing on 12 major organs, we found 861 genes specifically expressing in these tissues. Furthermore, 187 significantly differentially expressed genes were found among six types of muscle tissues. All expression data were made available at our new website http://cattleExp.org, which can be freely accessed for future gene expression analyses. Conclusions The housekeeping genes and tissue-specific genes identified will provide more information for researchers studying gene expression in cattle. Implications The web-based cattle gene expression analysis tool will make it easy for researchers to access large public datasets. Users can easily access all publicly available RNA data and upload their own RNA-Seq data.

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

Biostatistics ◽

10.1093/biostatistics/kxv026 ◽

2015 ◽

Vol 17 (1) ◽

pp. 16-28 ◽

Cited By ~ 43

Author(s):

Laurent Jacob ◽

Johann A. Gagnon-Bartsch ◽

Terence P. Speed

Keyword(s):

Gene Expression ◽

Large Scale ◽

State Of The Art ◽

Synthetic Data ◽

Negative Control ◽

Expression Data ◽

Bioconductor Package ◽

Expression Studies ◽

Unobserved Factor ◽

Gene Expression Studies

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.

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Systematic Identification of Housekeeping Genes Possibly Used as References in Caenorhabditis elegans by Large-Scale Data Integration

Cells ◽

10.3390/cells9030786 ◽

2020 ◽

Vol 9 (3) ◽

pp. 786 ◽

Cited By ~ 1

Author(s):

Jingxin Tao ◽

Youjin Hao ◽

Xudong Li ◽

Huachun Yin ◽

Xiner Nie ◽

...

Keyword(s):

Gene Expression ◽

Caenorhabditis Elegans ◽

Gene Expression Data ◽

Reference Genes ◽

Large Scale ◽

Gene Array ◽

Housekeeping Genes ◽

Functional Enrichment ◽

Expression Data ◽

Data Types

For accurate gene expression quantification, normalization of gene expression data against reliable reference genes is required. It is known that the expression levels of commonly used reference genes vary considerably under different experimental conditions, and therefore, their use for data normalization is limited. In this study, an unbiased identification of reference genes in Caenorhabditis elegans was performed based on 145 microarray datasets (2296 gene array samples) covering different developmental stages, different tissues, drug treatments, lifestyle, and various stresses. As a result, thirteen housekeeping genes (rps-23, rps-26, rps-27, rps-16, rps-2, rps-4, rps-17, rpl-24.1, rpl-27, rpl-33, rpl-36, rpl-35, and rpl-15) with enhanced stability were comprehensively identified by using six popular normalization algorithms and RankAggreg method. Functional enrichment analysis revealed that these genes were significantly overrepresented in GO terms or KEGG pathways related to ribosomes. Validation analysis using recently published datasets revealed that the expressions of newly identified candidate reference genes were more stable than the commonly used reference genes. Based on the results, we recommended using rpl-33 and rps-26 as the optimal reference genes for microarray and rps-2 and rps-4 for RNA-sequencing data validation. More importantly, the most stable rps-23 should be a promising reference gene for both data types. This study, for the first time, successfully displays a large-scale microarray data driven genome-wide identification of stable reference genes for normalizing gene expression data and provides a potential guideline on the selection of universal internal reference genes in C. elegans, for quantitative gene expression analysis.

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