Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach

Deciphering neuronal deficit and protein profile changes in human brain organoids from patients with creatine transporter deficiency

Creatine transporter deficiency (CTD) is an X-linked disease caused by mutations in the SLC6A8 gene. The impaired creatine uptake in the brain results in intellectual disability, behavioral disorders, language delay, and seizures. In this work, we generated human brain organoids from induced pluripotent stem cells of healthy subjects and CTD patients. Brain organoids from CTD donors had reduced creatine uptake compared with those from healthy donors. The expression of neural progenitor cell markers SOX2 and PAX6 was reduced in CTD-derived organoids, while GSK3β, a key regulator of neurogenesis, was up-regulated. Shotgun proteomics combined with integrative bioinformatic and statistical analysis identified changes in the abundance of proteins associated with intellectual disability, epilepsy, and autism. Re-establishment of the expression of a functional SLC6A8 in CTD-derived organoids restored creatine uptake and normalized the expression of SOX2, GSK3β, and other key proteins associated with clinical features of CTD patients. Our brain organoid model opens new avenues for further characterizing the CTD pathophysiology and supports the concept that reinstating creatine levels in patients with CTD could result in therapeutic efficacy.

Dodecyl creatine ester improves cognitive function and identifies key protein drivers including KIF1A and PLCB1 in a mouse model of creatine transporter deficiency

Creatine transporter deficiency (CTD), a leading cause of intellectual disability is a result of the mutation in the gene encoding the creatine transporter SLC6A8, which prevents creatine uptake into the brain, causing mental retardation, expressive speech and language delay, autistic-like behavior and epilepsy. Preclinical in vitro and in vivo data indicate that dodecyl creatine ester (DCE) which increases the creatine brain content, might be a therapeutic option for CTD patients. To gain a better understanding of the pathophysiology and DCE treatment efficacy in CTD, this study focuses on the identification of biomarkers related to cognitive improvement in a Slc6a8 knockout mouse model (Slc6a8−/y) engineered to mimic the clinical features of CTD patients which have low brain creatine content. Shotgun proteomics analysis of 4,035 proteins in four different brain regions; the cerebellum, cortex, hippocampus (associated with cognitive functions) and brain stem, and muscle as a control, was performed in 24 mice. Comparison of the protein abundance in the four brain regions between DCE-treated intranasally Slc6a8−/y mice and wild type and DCE-treated Slc6a8−/y and vehicle group identified 14 biomarkers, shedding light on the mechanism of action of DCE. Integrative bioinformatics and statistical modeling identified key proteins in CTD, including KIF1A and PLCB1. The abundance of these proteins in the four brain regions was significantly correlated with both the object recognition and the Y-maze tests. Our findings suggest a major role for PLCB1, KIF1A, and associated molecules in the pathogenesis of CTD.

Mesenchymal stromal/stem cells modulate response to experimental sepsis-induced lung injury via regulation of miR-27a-5p in recipient mice

Introduction

Mesenchymal stromal cell (MSC) therapy mitigates lung injury and improves survival in murine models of sepsis. Precise mechanisms of therapeutic benefit remain poorly understood.

Objectives

To identify host-derived regulatory elements that may contribute to the therapeutic effects of MSCs, we profiled the microRNAome (miRNAome) and transcriptome of lungs from mice randomised to experimental polymicrobial sepsis-induced lung injury treated with either placebo or MSCs.

Methods and results

A total of 11 997 genes and 357 microRNAs (miRNAs) expressed in lungs were used to generate a statistical estimate of association between miRNAs and their putative mRNA targets; 1395 miRNA:mRNA significant association pairs were found to be differentially expressed (false discovery rate ≤0.05). MSC administration resulted in the downregulation of miR-27a-5p and upregulation of its putative target gene VAV3 (adjusted p=1.272E-161) in septic lungs. In human pulmonary microvascular endothelial cells, miR-27a-5p expression levels were increased while VAV3 was decreased following lipopolysaccharide (LPS) or tumour necrosis factor (TNF) stimulation. Transfection of miR-27a-5p mimic or inhibitor resulted in increased or decreased VAV3 message, respectively. Luciferase reporter assay demonstrated specific binding of miR-27a-5p to the 3′UTR of VAV3. miR27a-5p inhibition mitigated TNF-induced (1) delayed wound closure, increased (2) adhesion and (3) transendothelial migration but did not alter permeability. In vivo, cell infiltration was attenuated by intratracheal coinstillation of the miR-27a-5p inhibitor, but this did not protect against endotoxin-induced oedema formation.

Conclusions

Our data support involvement of miR-27a-5p and VAV3 in cellular adhesion and infiltration during acute lung injury and a potential role for miR-27a-based therapeutics for acute respiratory distress syndrome.

flowVS: channel-specific variance stabilization in flow cytometry

Background

Comparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances.

Results

We present a variance-stabilization algorithm, called flowVS, that removes the mean-variance correlations from cell populations identified in each fluorescence channel. flowVS transforms each channel from all samples of a data set by the inverse hyperbolic sine (asinh) transformation. For each channel, the parameters of the transformation are optimally selected by Bartlett’s likelihood-ratio test so that the populations attain homogeneous variances. The optimum parameters are then used to transform the corresponding channels in every sample. flowVS is therefore an explicit variance-stabilization method that stabilizes within-population variances in each channel by evaluating the homoskedasticity of clusters with a likelihood-ratio test.

With two publicly available datasets, we show that flowVS removes the mean-variance dependence from raw FC data and makes the within-population variance relatively homogeneous. We demonstrate that alternative transformation techniques such as flowTrans, flowScape, logicle, and FCSTrans might not stabilize variance. Besides flow cytometry, flowVS can also be applied to stabilize variance in microarray data. With a publicly available data set we demonstrate that flowVS performs as well as the VSN software, a state-of-the-art approach developed for microarrays.

Conclusions

The homogeneity of variance in cell populations across FC samples is desirable when extracting features uniformly and comparing cell populations with different levels of marker expressions. The newly developed flowVS algorithm solves the variance-stabilization problem in FC and microarrays by optimally transforming data with the help of Bartlett’s likelihood-ratio test. On two publicly available FC datasets, flowVS stabilizes within-population variances more evenly than the available transformation and normalization techniques. flowVS-based variance stabilization can help in performing comparison and alignment of phenotypically identical cell populations across different samples. flowVS and the datasets used in this paper are publicly available in Bioconductor.

Expression profiling of human melanocytes in response to UV-B irradiation

A comprehensive gene expression analysis of human melanocytes was performed assessing the transcriptional profile of dark melanocytes (DM) and light melanocytes (LM) at basal conditions and after UV-B irradiation at different time points (6, 12 and 24 h), and in culture with different keratinocyte-conditioned media (KCM + and KCM −). The data, previously published in [1], have been deposited in NCBI's Gene Expression Omnibus (GEO accession number: GSE70280).

Comparison of the Transcriptional Profiles of Melanocytes from Dark and Light Skinned Individuals under Basal Conditions and Following Ultraviolet-B Irradiation

We analysed the whole-genome transcriptional profile of 6 cell lines of dark melanocytes (DM) and 6 of light melanocytes (LM) at basal conditions and after ultraviolet-B (UVB) radiation at different time points to investigate the mechanisms by which melanocytes protect human skin from the damaging effects of UVB. Further, we assessed the effect of different keratinocyte-conditioned media (KCM+ and KCM-) on melanocytes. Our results suggest that an interaction between ribosomal proteins and the P53 signaling pathway may occur in response to UVB in both DM and LM. We also observed that DM and LM show differentially expressed genes after irradiation, in particular at the first 6h after UVB. These are mainly associated with inflammatory reactions, cell survival or melanoma. Furthermore, the culture with KCM+ compared with KCM- had a noticeable effect on LM. This effect includes the activation of various signaling pathways such as the mTOR pathway, involved in the regulation of cell metabolism, growth, proliferation and survival. Finally, the comparison of the transcriptional profiles between LM and DM under basal conditions, and the application of natural selection tests in human populations allowed us to support the significant evolutionary role of MIF and ATP6V0B in the pigmentary phenotype.

Combining a wavelet change point and the Bayes factor for analysing chromosomal interaction data

Over the past few decades we have witnessed great efforts to understand the cellular function at the cytoplasm level. Nowadays there is a growing interest in understanding the relationship between function and structure at the nuclear, chromosomal and sub-chromosomal levels. Data on chromosomal interactions that are now becoming available in unprecedented resolution and scale open the way to address this challenge. Consequently, there is a growing need for new methods and tools that will transform these data into knowledge and insights. Here, we have developed all the steps required for the analysis of chromosomal interaction data (Hi-C data). The result is a methodology which combines a wavelet change point with the Bayes factor for useful correction, segmentation and comparison of Hi-C data. We further developed chromoR, an R package that implements the methods presented here. The chromoR package provides researchers with a means to analyse chromosomal interaction data using statistical bioinformatics, offering a new and comprehensive solution to this task.

Modeling the Drosophila gene cluster regulation network for muscle development

The development of accurate and reliable dynamical modeling procedures that describe the time evolution of gene expression levels is a prerequisite to understanding and controlling the transcription process. We focused on data from DNA microarray time series for 20 Drosophila genes involved in muscle development during the embryonic stage. Genes with similar expression profiles were clustered on the basis of a translation-invariant and scale-invariant distance measure. The time evolution of these clusters was modeled using coupled differential equations. Three model structures involving a transcription term and a degradation term were tested. The parameters were identified in successive steps: network construction, parameter optimization, and parameter reduction. The solutions were evaluated on the basis of the data reproduction and the number of parameters, as well as on two biology-based requirements: the robustness with respect to parameter variations and the values of the expression levels not being unrealistically large upon extrapolation in time. Various solutions were obtained that satisfied all our evaluation criteria. The regulatory networks inferred from these solutions were compared with experimental data. The best solution has half of the experimental connections, which compares favorably with previous approaches. Biasing the network toward the experimental connections led to the identification of a model that is only slightly less good on the basis of the evaluation criteria. The non-uniqueness of the solutions and the variable agreement with experimental connections were discussed in the context of the different hypotheses underlying this type of approach.

A gene selection method for GeneChip array data with small sample sizes

Background

In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods.

Results

We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate.

Conclusion

Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.

Irgm1 protects hematopoietic stem cells by negative regulation of IFN signaling

The IFN-inducible immunity-related p47 GTPase Irgm1 has been linked to Crohn disease as well as susceptibility to tuberculosis. Previously we demonstrated that HSC quiescence and function are aberrant in mice lacking Irgm1. To investigate the molecular basis for these defects, we conducted microarray expression profiling of Irgm1-deficient HSCs. Cell-cycle and IFN-response genes are up-regulated in Irgm1(-/-) HSCs, consistent with dysregulated IFN signaling. To test the hypothesis that Irgm1 normally down-regulates IFN signaling in HSCs, we generated Irgm1(-/-)Ifngr1(-/-) and Irgm1(-/-)Stat1(-/-) double-knockout animals. Strikingly, hyperproliferation, self-renewal, and autophagy defects in Irgm1(-/-) HSCs were normalized in double-knockout animals. These defects were also abolished in Irgm1(-/-)Irgm3(-/-) double-knockout animals, indicating that Irgm1 may regulate Irgm3 activity. Furthermore, the number of HSCs was reduced in aged Irgm1(-/-) animals, suggesting that negative feedback inhibition of IFN signaling by Irgm1 is necessary to prevent hyperproliferation and depletion of the stem cell compartment. Collectively, our results indicate that Irgm1 is a powerful negative regulator of IFN-dependent stimulation in HSCs, with an essential role in preserving HSC number and function. The deleterious effects of excessive IFN signaling may explain how hematologic abnormalities arise in patients with inflammatory conditions.

Accurate quantification of functional analogy among close homologs

Correctly evaluating functional similarities among homologous proteins is necessary for accurate transfer of experimental knowledge from one organism to another, and is of particular importance for the development of animal models of human disease. While the fact that sequence similarity implies functional similarity is a fundamental paradigm of molecular biology, sequence comparison does not directly assess the extent to which two proteins participate in the same biological processes, and has limited utility for analyzing families with several parologous members. Nevertheless, we show that it is possible to provide a cross-organism functional similarity measure in an unbiased way through the exclusive use of high-throughput gene-expression data. Our methodology is based on probabilistic cross-species mapping of functionally analogous proteins based on Bayesian integrative analysis of gene expression compendia. We demonstrate that even among closely related genes, our method is able to predict functionally analogous homolog pairs better than relying on sequence comparison alone. We also demonstrate that the landscape of functional similarity is often complex and that definitive “functional orthologs” do not always exist. Even in these cases, our method and the online interface we provide are designed to allow detailed exploration of sources of inferred functional similarity that can be evaluated by the user.

Common ancestry is a central tenet of modern biology, as genes from different species often show a high degree of sequence similarity, making it possible to study analogous processes across model organisms. However, many genes belong to large families with several duplicates and the relationship between genes from different species is often not one-to-one, complicating the transfer of experimental knowledge. We present a method that uses a large compendia of high-throughput expression data, that covers many genes that have not been analyzed in any other way, to systematically predict which genes are most likely to participate in the same biological process and thus have analogous function in different organisms. We show that our method agrees well with current experimental knowledge and we use it to investigate several families of genes that demonstrate the complexity of functional analogy.

Importance of replication in analyzing time-series gene expression data: corticosteroid dynamics and circadian patterns in rat liver

Background

Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However, experimental data usually contain potential sources of uncertainty and thus many experiments are now designed with repeated measurements to better assess such inherent variability. Many computational methods have been proposed to account for the variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information so that a variety of computational models that take the expression profiles as the input data can explore this information without any modification.

Results

We propose a methodology which integrates replicate variability into expression profiles, to generate so-called 'true' expression profiles. The study addresses two issues: (i) develop a statistical model that can estimate 'true' expression profiles which are more robust than the average profile, and (ii) extend our previous micro-clustering which was designed specifically for clustering time-series expression data. The model utilizes a previously proposed error model and the concept of 'relative difference'. The clustering effectiveness is demonstrated through synthetic data where several methods are compared. We subsequently analyze in vivo rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.

Conclusions

We have proposed a model which integrates the error information from repeated measurements into the expression profiles. Through numerous synthetic and real time-series data, we demonstrated the ability of the approach to improve the clustering performance and assist in the identification and selection of informative expression motifs.

Novel statistical framework to identify differentially expressed genes allowing transcriptomic background differences

MOTIVATION

Tests of differentially expressed genes (DEGs) from microarray experiments are based on the null hypothesis that genes that are irrelevant to the phenotype/stimulus are expressed equally in the target and control samples. However, this strict hypothesis is not always true, as there can be several transcriptomic background differences between target and control samples, including different cell/tissue types, different cell cycle stages and different biological donors. These differences lead to increased false positives, which have little biological/medical significance.

RESULT

In this article, we propose a statistical framework to identify DEGs between target and control samples from expression microarray data allowing transcriptomic background differences between these samples by introducing a modified null hypothesis that the gene expression background difference is normally distributed. We use an iterative procedure to perform robust estimation of the null hypothesis and identify DEGs as outliers. We evaluated our method using our own triplicate microarray experiment, followed by validations with reverse transcription-polymerase chain reaction (RT-PCR) and on the MicroArray Quality Control dataset. The evaluations suggest that our technique (i) results in less false positive and false negative results, as measured by the degree of agreement with RT-PCR of the same samples, (ii) can be applied to different microarray platforms and results in better reproducibility as measured by the degree of DEG identification concordance both intra- and inter-platforms and (iii) can be applied efficiently with only a few microarray replicates. Based on these evaluations, we propose that this method not only identifies more reliable and biologically/medically significant DEG, but also reduces the power-cost tradeoff problem in the microarray field.

AVAILABILITY

Source code and binaries freely available for download at http://comonca.org.cn/fdca/resources/softwares/deg.zip.

Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling

Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in (1)H NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.

Variance estimation in the analysis of microarray data

Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t-statistic and other statistics are unreliable owing to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of freedom problem. In this context, it is commonly observed that the variance increases proportionally with the intensity level, which has led many researchers to assume that the variance is a function of the mean. Here we concentrate on estimation of the variance as a function of an unknown mean in two models: the constant coefficient of variation model and the quadratic variance-mean model. Because the means are unknown and estimated with few degrees of freedom, naive methods that use the sample mean in place of the true mean are generally biased because of the errors-in-variables phenomenon. We propose three methods for overcoming this bias. The first two are variations on the theme of the so-called heteroscedastic simulation-extrapolation estimator, modified to estimate the variance function consistently. The third class of estimators is entirely different, being based on semiparametric information calculations. Simulations show the power of our methods and their lack of bias compared with the naive method that ignores the measurement error. The methodology is illustrated by using microarray data from leukaemia patients.

A Comparison of Parametric and Nonparametric Methods for Normalising cDNA Microarray Data

Normalisation is an essential first step in the analysis of most cDNA microarray data, to correct for effects arising from imperfections in the technology. Loess smoothing is commonly used to correct for trends in log-ratio data. However, parametric models, such as the additive plus multiplicative variance model, have been preferred for scale normalisation, though the variance structure of microarray data may be of a more complex nature than can be accommodated by a parametric model. We propose a new nonparametric approach that incorporates location and scale normalisation simultaneously using a Generalised Additive Model for Location, Scale and Shape (GAMLSS, Rigby and Stasinopoulos, 2005, Applied Statistics, 54, 507-554). We compare its performance in inferring differential expression with Huber et al.'s (2002, Bioinformatics, 18, 96-104) arsinh variance stabilising transformation (AVST) using real and simulated data. We show GAMLSS to be as powerful as AVST when the parametric model is correct, and more powerful when the model is wrong.