Assessing Differential Gene Expression with Small Sample Sizes in Oligonucleotide Arrays Using a Mean-Variance Model

Biometrics ◽  
2007 ◽  
Vol 63 (1) ◽  
pp. 41-49 ◽  
Author(s):  
Jianhua Hu ◽  
Fred A. Wright
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Small Sample ◽  
Sample Sizes ◽  
Variance Model ◽  
Oligonucleotide Arrays ◽  
Differential Gene ◽  
Small Sample Sizes ◽  
Mean Variance

scholarly journals Analysis of differential gene expression in human melanocytic tumour lesions by custom made oligonucleotide arrays

2005 ◽  
Vol 92 (12) ◽  
pp. 2249-2261 ◽  
Author(s):  
N J W de Wit ◽  
J Rijntjes ◽  
J H S Diepstra ◽  
T H van Kuppevelt ◽  
U H Weidle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Oligonucleotide Arrays ◽  
Custom Made ◽  
Differential Gene

P–555 Recurrent pregnancy loss is associated with changes in the pre-pregnant endometrial gland transcriptome

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
J Pearson-Farr ◽  
R Lewis ◽  
J Cleal ◽  
Y Cheong
Keyword(s):  
Gene Expression ◽  
Observational Study ◽  
Differential Gene Expression ◽  
Pregnancy Loss ◽  
Recurrent Pregnancy Loss ◽  
Small Sample ◽  
Specific Cell ◽  
Endometrial Gland ◽  
Egg Donor ◽  
Differential Gene

Abstract Study question Do endometrial gland factors influence recurrent pregnancy loss? Summary answer The endometrial gland transcriptome during the window of implantation is altered in women with recurrent pregnancy loss compared to controls. What is known already Secretions from endometrial glands contribute to the uterine environment that supports the attachment and implantation of the embryo in early pregnancy. Studies have attempted to identify an endometrial gene expression pattern associated with recurrent pregnancy loss however, the cellular heterogeneity within the endometrium may obscure important differences in specific cell populations. Study design, size, duration An observational study comparing controls and women with recurrent pregnancy loss. Participants/materials, setting, methods Endometrial samples were collected during the implantation period of the menstrual cycle from five matched participant egg donor controls and women with recurrent pregnancy loss. Endometrial glands were isolated from fresh endometrial biopsies and RNA sequencing was performed. A differential gene expression analysis and a gene ontology enrichment analysis was performed between egg donor controls and women with recurrent pregnancy loss. Main results and the role of chance This study reports a glandular epithelium specific gene expression profile and demonstrates differential gene expression of endometrial glands from women with recurrent pregnancy loss compared to controls. 18 genes were upregulated and 1 gene was downregulated in the endometrial glands from women with recurrent pregnancy loss compared to controls (5% false discovery rate). Biological processes which contain genes that were differentially expressed in women with recurrent pregnancy loss compared to controls include epithelial cell migration and regulation of secretion by the cell. Limitations, reasons for caution This is an observational study with a relatively small sample size. Wider implications of the findings: This study identified differences in gene expression in women with recurrent pregnancy loss that are specifically associated with endometrial glands rather than endometrium as a whole. These differences could be used to identify a perturbed endometrium, isolate causes of recurrent pregnancy loss and develop targeted therapies. Trial registration number Not applicable

The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Yanming Di ◽  
Daniel W Schafer ◽  
Jason S Cumbie ◽  
Jeff H Chang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Negative Binomial ◽  
Small Sample Size ◽  
Small Sample ◽  
Dispersion Parameter ◽  
Additional Parameter ◽  
Rna Seq ◽  
Data Set ◽  
Differential Gene

We propose a new statistical test for assessing differential gene expression using RNA sequencing (RNA-Seq) data. Commonly used probability distributions, such as binomial or Poisson, cannot appropriately model the count variability in RNA-Seq data due to overdispersion. The small sample size that is typical in this type of data also prevents the uncritical use of tools derived from large-sample asymptotic theory. The test we propose is based on the NBP parameterization of the negative binomial distribution. It extends an exact test proposed by Robinson and Smyth (2007, 2008). In one version of Robinson and Smyth’s test, a constant dispersion parameter is used to model the count variability between biological replicates. We introduce an additional parameter to allow the dispersion parameter to depend on the mean. Our parametric method complements nonparametric regression approaches for modeling the dispersion parameter. We apply the test we propose to an Arabidopsis data set and a range of simulated data sets. The results show that the test is simple, powerful and reasonably robust against departures from model assumptions.

A quantum leap in the reproducibility, precision, and sensitivity of gene expression profile analysis even when sample size is extremely small

2015 ◽  
Vol 13 (04) ◽  
pp. 1550018 ◽  
Author(s):  
Kevin Lim ◽  
Zhenhua Li ◽  
Kwok Pui Choi ◽  
Limsoon Wong
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Small Sample Size ◽  
Statistical Tests ◽  
Transcript Level ◽  
Small Sample ◽  
Sample Sizes ◽  
Quantum Leap ◽  
Two Samples ◽  
Small Sample Sizes

Transcript-level quantification is often measured across two groups of patients to aid the discovery of biomarkers and detection of biological mechanisms involving these biomarkers. Statistical tests lack power and false discovery rate is high when sample size is small. Yet, many experiments have very few samples (≤ 5). This creates the impetus for a method to discover biomarkers and mechanisms under very small sample sizes. We present a powerful method, ESSNet, that is able to identify subnetworks consistently across independent datasets of the same disease phenotypes even under very small sample sizes. The key idea of ESSNet is to fragment large pathways into smaller subnetworks and compute a statistic that discriminates the subnetworks in two phenotypes. We do not greedily select genes to be included based on differential expression but rely on gene-expression-level ranking within a phenotype, which is shown to be stable even under extremely small sample sizes. We test our subnetworks on null distributions obtained by array rotation; this preserves the gene–gene correlation structure and is suitable for datasets with small sample size allowing us to consistently predict relevant subnetworks even when sample size is small. For most other methods, this consistency drops to less than 10% when we test them on datasets with only two samples from each phenotype, whereas ESSNet is able to achieve an average consistency of 58% (72% when we consider genes within the subnetworks) and continues to be superior when sample size is large. We further show that the subnetworks identified by ESSNet are highly correlated to many references in the biological literature. ESSNet and supplementary material are available at: http://compbio.ddns.comp.nus.edu.sg:8080/essnet .

scholarly journals Evaluation of TagSeq, a reliable low-cost alternative for RNAseq

2016 ◽  
Author(s):  
Brian Keith Lohman ◽  
Jesse N Weber ◽  
Daniel I Bolnick
Keyword(s):  
Gene Expression ◽  
Statistical Power ◽  
Low Cost ◽  
Ecological Genetics ◽  
Small Sample ◽  
Sample Sizes ◽  
Experimental Conditions ◽  
Efficient Alternative ◽  
Highly Correlated ◽  
Small Sample Sizes

RNAseq is a relatively new tool for ecological genetics that offers researchers insight into changes in gene expression in response to a myriad of natural or experimental conditions. However, standard RNAseq methods (e.g., Illumina TruSeq® or NEBNext®) can be cost prohibitive, especially when study designs require large sample sizes. Consequently, RNAseq is often underused as a method, or is applied to small sample sizes that confer poor statistical power. Low cost RNAseq methods could therefore enable far greater and more powerful applications of transcriptomics in ecological genetics and beyond. Standard mRNAseq is costly partly because one sequences portions of the full length of all transcripts. Such whole-mRNA data is redundant for estimates of relative gene expression. TagSeq is an alternative method that focuses sequencing effort on mRNAs 3-prime end, thereby reducing the necessary sequencing depth per sample, and thus cost. Here we present a revised TagSeq protocol, and compare its performance against NEBNext®, the gold-standard whole mRNAseq method. We built both TagSeq and NEBNext® libraries from the same biological samples, each spiked with control RNAs. We found that TagSeq measured the control RNA distribution more accurately than NEBNext®, for a fraction of the cost per sample (~10%). The higher accuracy of TagSeq was particularly apparent for transcripts of moderate to low abundance. Technical replicates of TagSeq libraries are highly correlated, and were correlated with NEBNext® results. Overall, we show that our modified TagSeq protocol is an efficient alternative to traditional whole mRNAseq, offering researchers comparable data at greatly reduced cost.

scholarly journals Use of gene expression studies to investigate the human immunological response to malaria infection

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Susanne H. Hodgson ◽  
Julius Muller ◽  
Helen E. Lockstone ◽  
Adrian V. S. Hill ◽  
Kevin Marsh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Study Design ◽  
Malaria Infection ◽  
Small Sample ◽  
Sample Type ◽  
Sample Sizes ◽  
Human Immune Response ◽  
Human Immune ◽  
Small Sample Sizes

Abstract Background Transcriptional profiling of the human immune response to malaria has been used to identify diagnostic markers, understand the pathogenicity of severe disease and dissect the mechanisms of naturally acquired immunity (NAI). However, interpreting this body of work is difficult given considerable variation in study design, definition of disease, patient selection and methodology employed. This work details a comprehensive review of gene expression profiling (GEP) of the human immune response to malaria to determine how this technology has been applied to date, instances where this has advanced understanding of NAI and the extent of variability in methodology between studies to allow informed comparison of data and interpretation of results. Methods Datasets from the gene expression omnibus (GEO) including the search terms; ‘plasmodium’ or ‘malaria’ or ‘sporozoite’ or ‘merozoite’ or ‘gametocyte’ and ‘Homo sapiens’ were identified and publications analysed. Datasets of gene expression changes in relation to malaria vaccines were excluded. Results Twenty-three GEO datasets and 25 related publications were included in the final review. All datasets related to Plasmodium falciparum infection, except two that related to Plasmodium vivax infection. The majority of datasets included samples from individuals infected with malaria ‘naturally’ in the field (n = 13, 57%), however some related to controlled human malaria infection (CHMI) studies (n = 6, 26%), or cells stimulated with Plasmodium in vitro (n = 6, 26%). The majority of studies examined gene expression changes relating to the blood stage of the parasite. Significant heterogeneity between datasets was identified in terms of study design, sample type, platform used and method of analysis. Seven datasets specifically investigated transcriptional changes associated with NAI to malaria, with evidence supporting suppression of the innate pro-inflammatory response as an important mechanism for this in the majority of these studies. However, further interpretation of this body of work was limited by heterogeneity between studies and small sample sizes. Conclusions GEP in malaria is a potentially powerful tool, but to date studies have been hypothesis generating with small sample sizes and widely varying methodology. As CHMI studies are increasingly performed in endemic settings, there will be growing opportunity to use GEP to understand detailed time-course changes in host response and understand in greater detail the mechanisms of NAI.

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