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author:("Holden, marie")
1.  CD14 and Complement Crosstalk and Largely Mediate the Transcriptional Response to Escherichia coli in Human Whole Blood as Revealed by DNA Microarray 
PLoS ONE  2015;10(2):e0117261.
Systemic inflammation like in sepsis is still lacking specific diagnostic markers and effective therapeutics. The first line of defense against intruding pathogens and endogenous damage signals is pattern recognition by e.g., complement and Toll-like receptors (TLR). Combined inhibition of a key complement component (C3 and C5) and TLR-co-receptor CD14 has been shown to attenuate certain systemic inflammatory responses. Using DNA microarray and gene annotation analyses, we aimed to decipher the effect of combined inhibition of C3 and CD14 on the transcriptional response to bacterial challenge in human whole blood. Importantly, combined inhibition reversed the transcriptional changes of 70% of the 2335 genes which significantly responded to heat-inactivated Escherichia coli by on average 80%. Single inhibition was less efficient (p<0.001) but revealed a suppressive effect of C3 on 21% of the responding genes which was partially counteracted by CD14. Furthermore, CD14 dependency of the Escherichia coli-induced response was increased in C5-deficient compared to C5-sufficient blood. The observed crucial distinct and synergistic roles for complement and CD14 on the transcriptional level correspond to their broad impact on the inflammatory response in human blood, and their combined inhibition may become inevitable in the early treatment of acute systemic inflammation.
PMCID: PMC4338229  PMID: 25706641
2.  A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions 
Nucleic Acids Research  2014;42(18):e143.
Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher's exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.
PMCID: PMC4191384  PMID: 25114054
3.  Deep Sequencing the MicroRNA Transcriptome in Colorectal Cancer 
PLoS ONE  2013;8(6):e66165.
Colorectal cancer (CRC) is one of the leading causes of cancer related deaths and the search for prognostic biomarkers that might improve treatment decisions is warranted. MicroRNAs (miRNAs) are short non-coding RNA molecules involved in regulating gene expression and have been proposed as possible biomarkers in CRC. In order to characterize the miRNA transcriptome, a large cohort including 88 CRC tumors with long-term follow-up was deep sequenced. 523 mature miRNAs were expressed in our cohort, and they exhibited largely uniform expression patterns across tumor samples. Few associations were found between clinical parameters and miRNA expression, among them, low expression of miR-592 and high expression of miR-10b-5p and miR-615-3p were associated with tumors located in the right colon relative to the left colon and rectum. High expression of miR-615-3p was also associated with poorly differentiated tumors. No prognostic biomarker candidates for overall and metastasis-free survival were identified by applying the LASSO method in a Cox proportional hazards model or univariate Cox. Examination of the five most abundantly expressed miRNAs in the cohort (miR-10a-5p, miR-21-5p, miR-22-3p, miR-143-3p and miR-192-5p) revealed that their collective expression represented 54% of the detected miRNA sequences. Pathway analysis of the target genes regulated by the five most highly expressed miRNAs uncovered a significant number of genes involved in the CRC pathway, including APC, TGFβ and PI3K, thus suggesting that these miRNAs are relevant in CRC.
PMCID: PMC3688869  PMID: 23824282
4.  The Genomic HyperBrowser: an analysis web server for genome-scale data 
Nucleic Acids Research  2013;41(Web Server issue):W133-W141.
The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser ( is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.
PMCID: PMC3692097  PMID: 23632163
5.  Treatment outcome of chronic low back pain and radiographic lumbar disc degeneration are associated with inflammatory and matrix degrading gene variants: a prospective genetic association study 
Inflammatory and matrix degrading gene variants have been reported to be associated with disc degeneration. Some of these variants also modulate peripheral pain. This study examines the association of these genetic variants with radiographic lumbar disc degeneration and changes in pain and disability at long-term after surgical and cognitive behavioural management.
93 unrelated patients with chronic low back pain (CLBP) for duration of >1 year and lumbar disc degeneration were treated with lumbar fusion or cognitive intervention and exercises. Standardised questionnaires included the Oswestry Disability Index (ODI) and Visual Analog Score (VAS) for CLBP, were filled in by patients both at baseline and at 9 years follow-up. Degenerative changes at baseline Magnetic Resonance Imaging and Computed Tomography scans, were graded as moderate and severe (N=79). Yield and quality of blood and saliva DNA was assessed by nano drop spectrophotometry. Eight SNPs in 5 inflammatory and matrix degrading genes were successfully genotyped. Single marker and haplotype association with severity of degeneration, number of discs involved, changes in ODI and VAS CLBP, was done using Haploview, linear regression and R-package Haplostats.
Association analysis of individual SNPs revealed association of IL18RAP polymorphism rs1420100 with severe degeneration (p = 0.05) and more than one degenerated disc (p = 0.02). From the same gene two SNPs, rs917997 and rs1420106, were found to be in strong linkage disequilibrium (LD) and were associated with post treatment improvement in disability (p = 0.02). Haplotype association analysis of 5 SNPs spanning across IL18RAP, IL18R1 and IL1A genes revealed significant associations with improvement in disability (p=0.02) and reduction in pain (p=0.04). An association was found between MMP3 polymorphism rs72520913 and improvement in pain (p = 0.03) and with severe degeneration (p = 0.006).
The findings of the current study suggest a role of variation at inflammatory and matrix degrading genes with severity of lumbar disc degeneration, pain and disability.
PMCID: PMC3610293  PMID: 23522322
6.  Stress associated gene expression in blood cells is related to outcome in radiotherapy treated head and neck cancer patients 
BMC Cancer  2012;12:426.
We previously observed that a radiotherapy-induced biochemical response in plasma was associated with favourable outcome in head and neck squamous carcinoma cancer (HNSCC) patients. The aim of the present study was to compare stress associated blood cell gene expression between two sub-groups of HNSCC patients with different biochemical responses to radiotherapy.
Out of 87 patients (histologically verified), 10 biochemical ‘responders’ having a high relative increase in plasma oxidative damage and a concomitant decrease in plasma antioxidants during radiotherapy and 10 ‘poor-responders’ were selected for gene-expression analysis and compared using gene set enrichment analysis.
There was a significant induction of stress-relevant gene-sets in the responders following radiotherapy compared to the poor-responders. The relevance of the involvement of similar stress associated gene expression for HNSCC cancer and radioresistance was verified using two publicly available data sets of 42 HNSCC cases and 14 controls (GEO GSE6791), and radiation resistant and radiation sensitive HNSCC xenografts (E-GEOD-9716).
Radiotherapy induces a systemic stress response, as revealed by induction of stress relevant gene expression in blood cells, which is associated to favourable outcome in a cohort of 87 HNSCC patients. Whether these changes in gene expression reflects a systemic effect or are biomarkers of the tumour micro-environmental status needs further study.
Trial registration
Raw data are available at ArrayExpress under accession number E-MEXP-2460.
PMCID: PMC3517770  PMID: 23009663
Radiotherapy; HNSCC; Antioxidants; Microarray; GSEA; Cancer
7.  Genetic contribution of catechol-O-methyltransferase variants in treatment outcome of low back pain: a prospective genetic association study 
Treatment outcome of low back pain (LBP) is associated with inter-individual variations in pain relief and functional disability. Genetic variants of catechol-O-methyltransferase (COMT) gene have previously been shown to be associated with pain sensitivity and pain medication. This study examines the association between COMT polymorphisms and 7–11 year change in Oswestry Disability Index (ODI) and Visual Analog Score (VAS) for LBP as clinical outcome variables in patients treated with surgical instrumented lumbar fusion or cognitive intervention and exercise.
93 unrelated patients with chronic LBP for duration of >1 year and lumbar disc degeneration (LDD) were treated with lumbar fusion (N = 60) or cognitive therapy and exercises (N = 33). Standardised questionnaires assessing the ODI, VAS LBP, psychological factors and use of analgesics, were answered by patients both at baseline and at 7–11 years follow-up. Four SNPs in the COMT gene were successfully genotyped. Single marker as well as haplotype association with change in ODI and VAS LBP, were analyzed using Haploview, linear regression and R-package Haplostats. P-values were not formally corrected for multiple testing as this was an explorative study.
Association analysis of individual SNPs adjusted for covariates revealed association of rs4633 and rs4680 with post treatment improvement in VAS LBP (p = 0.02, mean difference (β) = 13.5 and p = 0.02, β = 14.2 respectively). SNPs, rs4633 and rs4680 were found to be genotypically similar and in strong linkage disequilibrium (LD). A significant association was found with covariates, analgesics (p = 0.001, β = 18.6); anxiety and depression (p = 0.008, β = 15.4) and age (p = 0.03, mean difference per year (β) = 0.7) at follow-up. There was a tendency for better improvement among heterozygous patients compared to the homozygous. No association was observed for the analysis of the common haplotypes, these SNPs were situated on.
Results suggest an influence of genetic variants of COMT gene in describing the variation in pain after treatment for low back pain. Replication in large samples with testing for other pain related genes is warranted.
PMCID: PMC3453507  PMID: 22612913
8.  The differential disease regulome 
BMC Genomics  2011;12:353.
Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information.
We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.
The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.
The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available.
We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at
PMCID: PMC3160420  PMID: 21736759
9.  The Genomic HyperBrowser: inferential genomics at the sequence level 
Genome Biology  2010;11(12):R121.
The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at
PMCID: PMC3046481  PMID: 21182759
10.  Blood cell gene expression associated with cellular stress defense is modulated by antioxidant-rich food in a randomised controlled clinical trial of male smokers 
BMC Medicine  2010;8:54.
Plant-based diets rich in fruit and vegetables can prevent development of several chronic age-related diseases. However, the mechanisms behind this protective effect are not elucidated. We have tested the hypothesis that intake of antioxidant-rich foods can affect groups of genes associated with cellular stress defence in human blood cells. Trial registration number: NCT00520819
In an 8-week dietary intervention study, 102 healthy male smokers were randomised to either a diet rich in various antioxidant-rich foods, a kiwifruit diet (three kiwifruits/d added to the regular diet) or a control group. Blood cell gene expression profiles were obtained from 10 randomly selected individuals of each group. Diet-induced changes on gene expression were compared to controls using a novel application of the gene set enrichment analysis (GSEA) on transcription profiles obtained using Affymetrix HG-U133-Plus 2.0 whole genome arrays.
Changes were observed in the blood cell gene expression profiles in both intervention groups when compared to the control group. Groups of genes involved in regulation of cellular stress defence, such as DNA repair, apoptosis and hypoxia, were significantly upregulated (GSEA, FDR q-values < 5%) by both diets compared to the control group. Genes with common regulatory motifs for aryl hydrocarbon receptor (AhR) and AhR nuclear translocator (AhR/ARNT) were upregulated by both interventions (FDR q-values < 5%). Plasma antioxidant biomarkers (polyphenols/carotenoids) increased in both groups.
The observed changes in the blood cell gene expression profiles suggest that the beneficial effects of a plant-based diet on human health may be mediated through optimization of defence processes.
PMCID: PMC2955589  PMID: 20846424
11.  Gene Dosage, Expression, and Ontology Analysis Identifies Driver Genes in the Carcinogenesis and Chemoradioresistance of Cervical Cancer 
PLoS Genetics  2009;5(11):e1000719.
Integrative analysis of gene dosage, expression, and ontology (GO) data was performed to discover driver genes in the carcinogenesis and chemoradioresistance of cervical cancers. Gene dosage and expression profiles of 102 locally advanced cervical cancers were generated by microarray techniques. Fifty-two of these patients were also analyzed with the Illumina expression method to confirm the gene expression results. An independent cohort of 41 patients was used for validation of gene expressions associated with clinical outcome. Statistical analysis identified 29 recurrent gains and losses and 3 losses (on 3p, 13q, 21q) associated with poor outcome after chemoradiotherapy. The intratumor heterogeneity, assessed from the gene dosage profiles, was low for these alterations, showing that they had emerged prior to many other alterations and probably were early events in carcinogenesis. Integration of the alterations with gene expression and GO data identified genes that were regulated by the alterations and revealed five biological processes that were significantly overrepresented among the affected genes: apoptosis, metabolism, macromolecule localization, translation, and transcription. Four genes on 3p (RYBP, GBE1) and 13q (FAM48A, MED4) correlated with outcome at both the gene dosage and expression level and were satisfactorily validated in the independent cohort. These integrated analyses yielded 57 candidate drivers of 24 genetic events, including novel loci responsible for chemoradioresistance. Further mapping of the connections among genetic events, drivers, and biological processes suggested that each individual event stimulates specific processes in carcinogenesis through the coordinated control of multiple genes. The present results may provide novel therapeutic opportunities of both early and advanced stage cervical cancers.
Author Summary
Genetic gains and losses, i.e. changes in gene dosages, are common abnormalities of human cancers. Discovering these defects and understanding the biological meaning can lead to improved therapeutic opportunities. This paper reports a large scale screening of gene dosage alterations in cervical cancer and gives a broader exploration of the expression and function of genes with gains or losses. We have focused on the most frequent gene dosage alterations and the alterations associated with survival after chemoradiotherapy, since these defects are likely to be of major importance for developing disease. The most notable finding was the discovery of a set of biological processes that are known hallmarks of cancer and were associated with gains and losses of specific genes. Moreover, novel loci associated with chemoradioresistance independent of existing clinical markers were found, and the genes involved were depicted. Our results indicated that gene dosage alterations play a causative role in the carcinogenesis and chemoradioresistance of cervical cancer and pinpointed candidate biomarkers of the disease.
PMCID: PMC2768783  PMID: 19911042
12.  Validation of oligoarrays for quantitative exploration of the transcriptome 
BMC Genomics  2008;9:258.
Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE.
A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·105 (oligoarrays), 1.1·105 (MPSS) and 7.6·104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1·106 (oligoarrays), 2.8·105 (MPSS) and 3.8·105 (SAGE).
The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.
PMCID: PMC2430212  PMID: 18513391
13.  Whole blood gene expression in infants with respiratory syncytial virus bronchiolitis 
Respiratory syncytial virus (RSV) is a major cause of viral bronchiolitis in infants worldwide, and environmental, viral and host factors are all of importance for disease susceptibility and severity. To study the systemic host response to this disease we used the microarray technology to measure mRNA gene expression levels in whole blood of five male infants hospitalised with acute RSV, subtype B, bronchiolitis versus five one year old male controls exposed to RSV during infancy without bronchiolitis. The gene expression levels were further evaluated in a new experiment using quantitative real-time polymerase chain reaction (QRT-PCR) both in the five infants selected for microarray and in 13 other infants hospitalised with the same disease.
Among the 30 genes most differentially expressed by microarray nearly 50% were involved in immunological processes. We found the highly upregulated interferon, alpha-inducible protein 27 (IFI27) and the highly downregulated gene Charcot-Leyden crystal protein (CLC) to be the two most differentially expressed genes in the microarray study. When performing QRT-PCR on these genes IFI27 was upregulated in all but one infant, and CLC was downregulated in all 18 infants, and similar to that given by microarray.
The gene IFI27 is upregulated and the gene CLC is downregulated in whole blood of infants hospitalised with RSV, subtype B, bronchiolitis and is not reported before. More studies are needed to elucidate the specificity of these gene expressions in association with host response to this virus in bronchiolitis of moderate severity.
PMCID: PMC1713240  PMID: 17166282
14.  Limitations of mRNA amplification from small-size cell samples 
BMC Genomics  2005;6:147.
Global mRNA amplification has become a widely used approach to obtain gene expression profiles from limited material. An important concern is the reliable reflection of the starting material in the results obtained. This is especially important with extremely low quantities of input RNA where stochastic effects due to template dilution may be present. This aspect remains under-documented in the literature, as quantitative measures of data reliability are most often lacking. To address this issue, we examined the sensitivity levels of each transcript in 3 different cell sample sizes. ANOVA analysis was used to estimate the overall effects of reduced input RNA in our experimental design. In order to estimate the validity of decreasing sample sizes, we examined the sensitivity levels of each transcript by applying a novel model-based method, TransCount.
From expression data, TransCount provided estimates of absolute transcript concentrations in each examined sample. The results from TransCount were used to calculate the Pearson correlation coefficient between transcript concentrations for different sample sizes. The correlations were clearly transcript copy number dependent. A critical level was observed where stochastic fluctuations became significant. The analysis allowed us to pinpoint the gene specific number of transcript templates that defined the limit of reliability with respect to number of cells from that particular source. In the sample amplifying from 1000 cells, transcripts expressed with at least 121 transcripts/cell were statistically reliable and for 250 cells, the limit was 1806 transcripts/cell. Above these thresholds, correlation between our data sets was at acceptable values for reliable interpretation.
These results imply that the reliability of any amplification experiment must be validated empirically to justify that any gene exists in sufficient quantity in the input material. This finding has important implications for any experiment where only extremely small samples such as single cell analyses or laser captured microdissected cells are available.
PMCID: PMC1310617  PMID: 16253144
15.  Genome-wide estimation of transcript concentrations from spotted cDNA microarray data 
Nucleic Acids Research  2005;33(17):e143.
A method providing absolute transcript concentrations from spotted microarray intensity data is presented. Number of transcripts per µg total RNA, mRNA or per cell, are obtained for each gene, enabling comparisons of transcript levels within and between tissues. The method is based on Bayesian statistical modelling incorporating available information about the experiment from target preparation to image analysis, leading to realistically large confidence intervals for estimated concentrations. The method was validated in experiments using transcripts at known concentrations, showing accuracy and reproducibility of estimated concentrations, which were also in excellent agreement with results from quantitative real-time PCR. We determined the concentration for 10 157 genes in cervix cancers and a pool of cancer cell lines and found values in the range of 105–1010 transcripts per µg total RNA. The precision of our estimates was sufficiently high to detect significant concentration differences between two tumours and between different genes within the same tumour, comparisons that are not possible with standard intensity ratios. Our method can be used to explore the regulation of pathways and to develop individualized therapies, based on absolute transcript concentrations. It can be applied broadly, facilitating the construction of the transcriptome, continuously updating it by integrating future data.
PMCID: PMC1243803  PMID: 16204447
16.  Effects of mRNA amplification on gene expression ratios in cDNA experiments estimated by analysis of variance 
BMC Genomics  2003;4:11.
A limiting factor of cDNA microarray technology is the need for a substantial amount of RNA per labeling reaction. Thus, 20–200 micro-grams total RNA or 0.5–2 micro-grams poly (A) RNA is typically required for monitoring gene expression. In addition, gene expression profiles from large, heterogeneous cell populations provide complex patterns from which biological data for the target cells may be difficult to extract. In this study, we chose to investigate a widely used mRNA amplification protocol that allows gene expression studies to be performed on samples with limited starting material. We present a quantitative study of the variation and noise present in our data set obtained from experiments with either amplified or non-amplified material.
Using analysis of variance (ANOVA) and multiple hypothesis testing, we estimated the impact of amplification on the preservation of gene expression ratios. Both methods showed that the gene expression ratios were not completely preserved between amplified and non-amplified material. We also compared the expression ratios between the two cell lines for the amplified material with expression ratios between the two cell lines for the non-amplified material for each gene. With the aid of multiple t-testing with a false discovery rate of 5%, we found that 10% of the genes investigated showed significantly different expression ratios.
Although the ratios were not fully preserved, amplification may prove to be extremely useful with respect to characterizing low expressing genes.
PMCID: PMC153514  PMID: 12659661
mRNA amplification; microarray; gene expression; multiple hypothesis testing; linear mixed effects model

Results 1-16 (16)