BatchQC: interactive software for evaluating sample and batch effects in genomic data

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

BatchFLEX: feature-level equalization of X-batch

MOTIVATION

Integrative analysis of heterogeneous expression data remains challenging due to variations in platform, RNA quality, sample processing, and other unknown technical effects. Selecting the approach for removing unwanted batch effects can be a time-consuming and tedious process, especially for more biologically focused investigators.

RESULTS

Here, we present BatchFLEX, a Shiny app that can facilitate visualization and correction of batch effects using several established methods. BatchFLEX can visualize the variance contribution of a factor before and after correction. As an example, we have analyzed ImmGen microarray data and enhanced its expression signals that distinguishes each immune cell type. Moreover, our analysis revealed the impact of the batch correction in altering the gene expression rank and single-sample GSEA pathway scores in immune cell types, highlighting the importance of real-time assessment of the batch correction for optimal downstream analysis.

AVAILABILITY AND IMPLEMENTATION

Our tool is available through Github https://github.com/shawlab-moffitt/BATCH-FLEX-ShinyApp with an online example on Shiny.io https://shawlab-moffitt.shinyapps.io/batch_flex/.

Transcriptomic analysis of primary nasal epithelial cells reveals altered interferon signalling in preterm birth survivors at one year of age

Introduction: Many survivors of preterm birth (<37 weeks gestation) have lifelong respiratory deficits, the drivers of which remain unknown. Influencers of pathophysiological outcomes are often detectable at the gene level and pinpointing these differences can help guide targeted research and interventions. This study provides the first transcriptomic analysis of primary nasal airway epithelial cells in survivors of preterm birth at approximately 1 year of age. Methods: Nasal airway epithelial brushings were collected, and primary cell cultures established from term (>37 weeks gestation) and very preterm participants (≤32 weeks gestation). Ex vivo RNA was collected from brushings with sufficient cell numbers and in vitro RNA was extracted from cultured cells, with bulk RNA sequencing performed on both the sample types. Differential gene expression was assessed using the limma-trend pipeline and pathway enrichment identified using Reactome and GO analysis. To corroborate gene expression data, cytokine concentrations were measured in cell culture supernatant. Results: Transcriptomic analysis to compare term and preterm cells revealed 2,321 genes differentially expressed in ex vivo samples and 865 genes differentially expressed in cultured basal cell samples. Over one third of differentially expressed genes were related to host immunity, with interferon signalling pathways dominating the pathway enrichment analysis and IRF1 identified as a hub gene. Corroboration of disrupted interferon release showed that concentrations of IFN-α2 were below measurable limits in term samples but elevated in preterm samples [19.4 (76.7) pg/ml/µg protein, p = 0.03]. IFN-γ production was significantly higher in preterm samples [3.3 (1.5) vs. 9.4 (17.7) pg/ml/µg protein; p = 0.01] as was IFN-β [7.8 (2.5) vs. 13.6 (19.5) pg/ml/µg protein, p = 0.01]. Conclusion: Host immunity may be compromised in the preterm nasal airway epithelium in early life. Altered immune responses may lead to cycles of repeated infections, causing persistent inflammation and tissue damage which can have significant impacts on long-term respiratory function.

EPITHELIAL REMODELING AND MICROBIAL DYSBIOSIS IN THE LOWER RESPIRATORY TRACT OF VITAMIN A-DEFICIENT MOUSE LUNGS

The World Health Organization identified vitamin A deficiency (VAD) as a major public health issue in low-income communities and developing countries, while additional studies have shown dietary VAD leads to various lung pathologies. Once believed to be sterile, research now shows that transient microbial communities exist within healthy lungs and are often dysregulated in patients suffering from malnourishment, respiratory infections, and disease. The inability to parse vitamin A-mediated mechanisms from other metabolic mechanisms in humans with pathogenic endotypes, as well as the lack of data investigating how VAD affects the lung microbiome, remains a significant gap in the field. To address this unmet need, we compared molecular, metatranscriptomic, and morphometric data to identify how dietary VAD affects the lung as well as the lung microbiome. Our research shows structural and functional alterations in host-microbe-diet interactions in VAD lungs compared to vitamin A-sufficient (VAS) lungs; these changes are associated with epithelial remodeling, a breakdown in mucociliary clearance, microbial imbalance, and altered microbial colonization patterns after 8 weeks of vitamin A deficient diet. These findings confirm vitamin A is critical for lung homeostasis and provide mechanistic insights that could be valuable for the prevention of respiratory infections and disease.

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning

Immunotherapy is a promising strategy to treat cancer. Here, we present a protocol for analyzing the transcriptome-based phenotypic alterations and immune cell infiltration in the tumor microenvironment. We describe steps for integrating single-cell RNA sequencing (scRNA-seq) data, comparing phenotypes and origins of mononuclear phagocytes, inferring the differentiation trajectory and infiltration process, and identifying infiltration-associated genes using machine learning. We then detail procedures for exploring the impact of these genes in prognosis through the integrated microarray and bulk RNA-seq data to obtain potential drug targets. For complete details on the use and execution of this protocol, please refer to Liao et al.1.

BatchEval Pipeline: batch effect evaluation workflow for multiple datasets joint analysis

As genomic sequencing technology continues to advance, it becomes increasingly important to perform joint analyses of multiple datasets of transcriptomics. However, batch effect presents challenges for dataset integration, such as sequencing data measured on different platforms, and datasets collected at different times. Here, we report the development of BatchEval Pipeline, a batch effect workflow used to evaluate batch effect on dataset integration. The BatchEval Pipeline generates a comprehensive report, which consists of a series of HTML pages for assessment findings, including a main page, a raw dataset evaluation page, and several built-in methods evaluation pages. The main page exhibits basic information of the integrated datasets, a comprehensive score of batch effect, and the most recommended method for removing batch effect from the current datasets. The remaining pages exhibit evaluation details for the raw dataset, and evaluation results from the built-in batch effect removal methods after removing batch effect. This comprehensive report enables researchers to accurately identify and remove batch effects, resulting in more reliable and meaningful biological insights from integrated datasets. In summary, the BatchEval Pipeline represents a significant advancement in batch effect evaluation, and is a valuable tool to improve the accuracy and reliability of the experimental results.

Availability & Implementation

The source code of the BatchEval Pipeline is available at https://github.com/STOmics/BatchEval.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Background

The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear.

Methodology

In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response.

Results and discussion

The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers.

Conclusions

Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

MIF is a critical regulator of mononuclear phagocytic infiltration in hepatocellular carcinoma

Immunotherapy targeting tumor-associated macrophages (TAMs) is a promising approach to treating cancer. However, the limited drug targets and ambiguous mechanisms impede the development of clinical immunotherapy strategies. To elucidate the underlying processes involved in mononuclear phagocyte (MNP) infiltration and phenotypic changes in hepatocellular carcinoma (HCC), we integrated single-cell RNA-sequencing data from 100,030 cells derived from patients with HCC and healthy individuals and compared the phenotypes and origins of the MNPs in the tumor core, tumor periphery, adjacent normal tissue, and healthy liver samples. Using machine learning and multi-omics analyses, we identified 445 infiltration-associated genes and potential drug targets affecting this process. Through in vitro experiments, we found that the expression of macrophage migration inhibitory factor (MIF) is the upstream regulator of secreted phosphoprotein 1 (SPP1) and promote migration in TAMs. Our findings also indicate that MIF promotes tumor metastasis and invasion and is a promising potential target for treating HCC.

BEENE: deep learning-based nonlinear embedding improves batch effect estimation

MOTIVATION

Analyzing large-scale single-cell transcriptomic datasets generated using different technologies is challenging due to the presence of batch-specific systematic variations known as batch effects. Since biological and technological differences are often interspersed, detecting and accounting for batch effects in RNA-seq datasets are critical for effective data integration and interpretation. Low-dimensional embeddings, such as principal component analysis (PCA) are widely used in visual inspection and estimation of batch effects. Linear dimensionality reduction methods like PCA are effective in assessing the presence of batch effects, especially when batch effects exhibit linear patterns. However, batch effects are inherently complex and existing linear dimensionality reduction methods could be inadequate and imprecise in the presence of sophisticated nonlinear batch effects.

RESULTS

We present Batch Effect Estimation using Nonlinear Embedding (BEENE), a deep nonlinear auto-encoder network which is specially tailored to generate an alternative lower dimensional embedding suitable for both linear and nonlinear batch effects. BEENE simultaneously learns the batch and biological variables from RNA-seq data, resulting in an embedding that is more robust and sensitive than PCA embedding in terms of detecting and quantifying batch effects. BEENE was assessed on a collection of carefully controlled simulated datasets as well as biological datasets, including two technical replicates of mouse embryogenesis cells, peripheral blood mononuclear cells from three largely different experiments and five studies of pancreatic islet cells.

AVAILABILITY AND IMPLEMENTATION

BEENE is freely available as an open source project at https://github.com/ashiq24/BEENE.

Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference

Nonignorable technical variation is commonly observed across data from multiple experimental runs, platforms, or studies. These so-called batch effects can lead to difficulty in merging data from multiple sources, as they can severely bias the outcome of the analysis. Many groups have developed approaches for removing batch effects from data, usually by accommodating batch variables into the analysis (one-step correction) or by preprocessing the data prior to the formal or final analysis (two-step correction). One-step correction is often desirable due it its simplicity, but its flexibility is limited and it can be difficult to include batch variables uniformly when an analysis has multiple stages. Two-step correction allows for richer models of batch mean and variance. However, prior investigation has indicated that two-step correction can lead to incorrect statistical inference in downstream analysis. Generally speaking, two-step approaches introduce a correlation structure in the corrected data, which, if ignored, may lead to either exaggerated or diminished significance in downstream applications such as differential expression analysis. Here, we provide more intuitive and more formal evaluations of the impacts of two-step batch correction compared to existing literature. We demonstrate that the undesired impacts of two-step correction (exaggerated or diminished significance) depend on both the nature of the study design and the batch effects. We also provide strategies for overcoming these negative impacts in downstream analyses using the estimated correlation matrix of the corrected data. We compare the results of our proposed workflow with the results from other published one-step and two-step methods and show that our methods lead to more consistent false discovery controls and power of detection across a variety of batch effect scenarios. Software for our method is available through GitHub (https://github.com/jtleek/sva-devel) and will be available in future versions of the $\texttt{sva}$ R package in the Bioconductor project (https://bioconductor.org/packages/release/bioc/html/sva.html).

Assessing Biomaterial-Induced Stem Cell Lineage Fate by Machine Learning-Based Artificial Intelligence

Current functional assessment of biomaterial-induced stem cell lineage fate in vitro mainly relies on biomarker-dependent methods with limited accuracy and efficiency. Here a "Mesenchymal stem cell Differentiation Prediction (MeD-P)" framework for biomaterial-induced cell lineage fate prediction is reported. MeD-P contains a cell-type-specific gene expression profile as a reference by integrating public RNA-seq data related to tri-lineage differentiation (osteogenesis, chondrogenesis, and adipogenesis) of human mesenchymal stem cells (hMSCs) and a predictive model for classifying hMSCs differentiation lineages using the k-nearest neighbors (kNN) strategy. It is shown that MeD-P exhibits an overall accuracy of 90.63% on testing datasets, which is significantly higher than the model constructed based on canonical marker genes (80.21%). Moreover, evaluations of multiple biomaterials show that MeD-P provides accurate prediction of lineage fate on different types of biomaterials as early as the first week of hMSCs culture. In summary, it is demonstrated that MeD-P is an efficient and accurate strategy for stem cell lineage fate prediction and preliminary biomaterial functional evaluation.

Aging gene signature of IL-7 receptor alpha low effector memory CD8+ T cells is associated with neurocognitive functioning in Alzheimer's disease

CD45RA+ effector memory (EM) CD8+ T cell expansion was reported in Alzheimer's disease (AD). Such cells are IL-7 receptor alpha (IL-7Rα)low EM CD8+ T cells, which expand with age and have a unique aging gene signature (i.e., IL-7Rαlow aging genes). Here we investigated whether IL-7Rαlow aging genes and previously reported AD and memory (ADM) genes overlapped with clinical significance in AD patients. RT-qPCR analysis of 40 genes, including 29 ADM, 9 top IL-7Ralow aging and 2 control genes, showed 8 differentially expressed genes between AD and cognitively normal groups; five (62.5%) of which were top IL-7Rαlow aging genes. Over-representation analysis revealed that these genes were highly present in molecular and biological pathways associated with AD. Distinct expression levels of these genes were associated with neuropsychological testing performance in 3 subgroups of dementia participants. Our findings support the possible implication of the IL-7Rαlow aging gene signature with AD.

A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

Developmental exposure to real-life environmental chemical mixture programs a testicular dysgenesis syndrome-like phenotype in prepubertal lambs

Current declines in male reproductive health may, in part, be driven by anthropogenic environmental chemical (EC) exposure. Using a biosolids treated pasture (BTP) sheep model, this study examined the effects of gestational exposure to a translationally relevant EC mixture. Testes of 8-week-old ram lambs from mothers exposed to BTP during pregnancy contained fewer germ cells and had a greater proportion of Sertoli-cell-only seminiferous tubules. This concurs with previous published data from fetuses and neonatal lambs from mothers exposed to BTP. Comparison between the testicular transcriptome of biosolids lambs and human testicular dysgenesis syndrome (TDS) patients indicated common changes in genes involved in apoptotic and mTOR signalling. Gene expression data and immunohistochemistry indicated increased HIF1α activation and nuclear localisation in Leydig cells of BTP exposed animals. As HIF1α is reported to disrupt testosterone synthesis, these results provide a potential mechanism for the pathogenesis of this testicular phenotype, and TDS in humans.

Identification of critical genes associated with radiotherapy resistance in cervical cancer by bioinformatics

Background

Cervical cancer (CC) is one of the common malignant tumors in women, Currently, 30% of patients with intermediate to advanced squamous cervical cancer are still uncontrolled or recurrent after standard radical simultaneous radiotherapy; therefore, the search for critical genes affecting the sensitivity of radiotherapy may lead to new strategies for treatment.

Methods

Firstly, differentially expressed genes (DEGs) between radiotherapy-sensitivity and radiotherapy-resistance were identified by GEO2R from the gene expression omnibus (GEO) website, and prognosis-related genes for cervical cancer were obtained from the HPA database. Subsequently, the DAVID database analyzed gene ontology (GO). Meanwhile, the protein-protein interaction network was constructed by STRING; By online analysis of DEGs, prognostic genes, and CCDB data that are associated with cervical cancer formation through the OncoLnc database, we aim to search for the key DEGs associated with CC, Finally, the key gene(s) was further validated by immunohistochemistry.

Result

298 differentially expressed genes, 712 genes associated with prognosis, and 509 genes related to cervical cancer formation were found. The results of gene function analysis showed that DEGs were mainly significant in functional pathways such as variable shear and energy metabolism. By further verification, two genes, ASPH and NKAPP1 were identified through validation as genes that affect both sensitivities to radiotherapy and survival finally. Then, immunohistochemical results showed that the ASPH gene was highly expressed in the radiotherapy-resistant group and had lower Overall survival (OS) and Progression-free survival (PFS).

Conclusion

This study aims to better understand the characteristics of cervical cancer radiation therapy resistance-related genes through bioinformatics and provide further research ideas for finding new mechanisms and potential therapeutic targets related to cervical cancer radiation therapy.

Cytokine Activation Reveals Tissue-Imprinted Gene Profiles of Mesenchymal Stromal Cells

Development of standardized metrics to support manufacturing and regulatory approval of mesenchymal stromal cell (MSC) products is confounded by heterogeneity of MSC populations. Many reports describe fundamental differences between MSCs from various tissues and compare unstimulated and activated counterparts. However, molecular information comparing biological profiles of activated MSCs across different origins and donors is limited. To better understand common and source-specific mechanisms of action, we compared the responses of 3 donor populations each of human umbilical cord (UC) and bone marrow (BM) MSCs to TNF-α, IL-1β or IFN-γ. Transcriptome profiles were analysed by microarray and select secretome profiles were assessed by multiplex immunoassay. Unstimulated (resting) UC and BM-MSCs differentially expressed (DE) 174 genes. Signatures of TNF-α-stimulated BM and UC-MSCs included 45 and 14 new DE genes, respectively, while all but 7 of the initial 174 DE genes were expressed at comparable levels after licensing. After IL-1β activation, only 5 of the 174 DE genes remained significantly different, while 6 new DE genes were identified. IFN-γ elicited a robust transcriptome response from both cell types, yet nearly all differences (171/174) between resting populations were attenuated. Nine DE genes predominantly corresponding to immunogenic cell surface proteins emerged as a BM-MSC signature of IFN-γ activation. Changes in protein synthesis of select analytes correlated modestly with transcript levels. The dynamic responses of licensed MSCs documented herein, which attenuated heterogeneity between unstimulated populations, provide new insight into common and source-imprinted responses to cytokine activation and can inform strategic development of meaningful, standardized assays.

A transcriptionally repressed quiescence program is associated with paused RNAPII and is poised for cell cycle reentry

Adult stem cells persist in mammalian tissues by entering a state of reversible quiescence/ G0, associated with low transcription. Using cultured myoblasts and muscle stem cells, we report that in G0, global RNA content and synthesis are substantially repressed, correlating with decreased RNA Polymerase II (RNAPII) expression and activation. Integrating RNAPII occupancy and transcriptome profiling, we identify repressed networks and a role for promoter-proximal RNAPII pausing in G0. Strikingly, RNAPII shows enhanced pausing in G0 on repressed genes encoding regulators of RNA biogenesis (Nucleolin, Rps24, Ctdp1); release of pausing is associated with their increased expression in G1. Knockdown of these transcripts in proliferating cells leads to induction of G0 markers, confirming the importance of their repression in establishment of G0. A targeted screen of RNAPII regulators revealed that knockdown of Aff4 (positive regulator of elongation) unexpectedly enhances expression of G0-stalled genes and hastens S phase; NELF, a regulator of pausing appears to be dispensable. We propose that RNAPII pausing contributes to transcriptional control of a subset of G0-repressed genes to maintain quiescence and impacts the timing of the G0-G1 transition.

Gene Expression Monotonicity across Bladder Cancer Stages Informs on the Molecular Pathogenesis and Identifies a Prognostic Eight-Gene Signature

Despite advancements in molecular classification, tumor stage and grade still remain the most relevant prognosticators used by clinicians to decide on patient management. Here, we leverage publicly available data to characterize bladder cancer (BLCA)’s stage biology based on increased sample sizes, identify potential therapeutic targets, and extract putative biomarkers. A total of 1135 primary BLCA transcriptomes from 12 microarray studies were compiled in a meta-cohort and analyzed for monotonal alterations in pathway activities, gene expression, and co-expression patterns with increasing stage (Ta–T1–T2–T3–T4), starting from the non-malignant tumor-adjacent urothelium. The TCGA-2017 and IMvigor-210 RNA-Seq data were used to validate our findings. Wnt, MTORC1 signaling, and MYC activity were monotonically increased with increasing stage, while an opposite trend was detected for the catabolism of fatty acids, circadian clock genes, and the metabolism of heme. Co-expression network analysis highlighted stage- and cell-type-specific genes of potentially synergistic therapeutic value. An eight-gene signature, consisting of the genes AKAP7, ANLN, CBX7, CDC14B, ENO1, GTPBP4, MED19, and ZFP2, had independent prognostic value in both the discovery and validation sets. This novel eight-gene signature may increase the granularity of current risk-to-progression estimators.

Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1–48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.

To understand cancer, researchers need to know which molecules tumor cells use. These so-called ‘biomarkers’ tag cancer cells as being different from healthy cells, and can be used to predict how aggressive a tumor may be, or how well it might respond to treatment.

A popular technique for assessing biomarkers across multiple tumors is to use tissue microarrays. This involves taking samples from different tumors and embedding them in a block of wax, which is then cut into micro-thin slices and stained with reagents that can detect specific biomarkers, such as proteins. Each block contains hundreds of samples, which all experience the same conditions. So, any patterns detected in the staining are likely to represent real variations in the biomarkers present.

Many cancer studies, however, often compare samples from multiple tissue microarrays, which may increase the risk of technical artifacts: for example, staining may look stronger in one batch of tissue samples than another, even though the amount of biomarker present in these different arrays is roughly the same. These ‘batch effects’ could potentially bias the results of the experiment and lead to the identification of misleading patterns.

To evaluate how batch effects impact tissue microarray studies, Stopsack et al. examined 14 wax blocks which contained tumor samples from 1,448 men with prostate cancer. This revealed that for some biomarkers, but not others, there were noticeable differences between tissue microarrays that were clearly the result of batch effects. Stopsack et al. then tested six different ways of fixing these discrepancies using statistical methods. All six approaches were successful, even if the arrays included tumors with different characteristics, such as tumors that had been diagnosed more or less recently.

This work highlights the importance of considering batch effects when using tissue microarrays to study cancer. Stopsack et al. have used their statistical approaches to develop freely available software which can reduce the biases that sometimes arise from these technical artifacts. This could help researchers avoid misleading patterns in their data and make it easier to detect real variations in the biomarkers present between tumor samples.

A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction

The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management.

Epigenetic adaptations of the masticatory mucosa to periodontal inflammation

Background

In mucosal barrier interfaces, flexible responses of gene expression to long-term environmental changes allow adaptation and fine-tuning for the balance of host defense and uncontrolled not-resolving inflammation. Epigenetic modifications of the chromatin confer plasticity to the genetic information and give insight into how tissues use the genetic information to adapt to environmental factors. The oral mucosa is particularly exposed to environmental stressors such as a variable microbiota. Likewise, persistent oral inflammation is the most important intrinsic risk factor for the oral inflammatory disease periodontitis and has strong potential to alter DNA-methylation patterns. The aim of the current study was to identify epigenetic changes of the oral masticatory mucosa in response to long-term inflammation that resulted in periodontitis.

Methods and results

Genome-wide CpG methylation of both inflamed and clinically uninflamed solid gingival tissue biopsies of 60 periodontitis cases was analyzed using the Infinium MethylationEPIC BeadChip. We validated and performed cell-type deconvolution for infiltrated immune cells using the EpiDish algorithm. Effect sizes of DMPs in gingival epithelial and fibroblast cells were estimated and adjusted for confounding factors using our recently developed “intercept-method”. In the current EWAS, we identified various genes that showed significantly different methylation between periodontitis-inflamed and uninflamed oral mucosa in periodontitis patients. The strongest differences were observed for genes with roles in wound healing (ROBO2, PTP4A3), cell adhesion (LPXN) and innate immune response (CCL26, DNAJC1, BPI). Enrichment analyses implied a role of epigenetic changes for vesicle trafficking gene sets.

Conclusions

Our results imply specific adaptations of the oral mucosa to a persistent inflammatory environment that involve wound repair, barrier integrity, and innate immune defense.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01190-7.

Inhibition of colony stimulating factor 1 receptor corrects maternal inflammation-induced microglial and synaptic dysfunction and behavioral abnormalities

Maternal immune activation (MIA) disrupts the central innate immune system during a critical neurodevelopmental period. Microglia are primary innate immune cells in the brain although their direct influence on the MIA phenotype is largely unknown. Here we show that MIA alters microglial gene expression with upregulation of cellular protrusion/neuritogenic pathways, concurrently causing repetitive behavior, social deficits, and synaptic dysfunction to layer V intrinsically bursting pyramidal neurons in the prefrontal cortex of mice. MIA increases plastic dendritic spines of the intrinsically bursting neurons and their interaction with hyper-ramified microglia. Treating MIA offspring by colony stimulating factor 1 receptor inhibitors induces depletion and repopulation of microglia, and corrects protein expression of the newly identified MIA-associated neuritogenic molecules in microglia, which coalesces with correction of MIA-associated synaptic, neurophysiological, and behavioral abnormalities. Our study demonstrates that maternal immune insults perturb microglial phenotypes and influence neuronal functions throughout adulthood, and reveals a potent effect of colony stimulating factor 1 receptor inhibitors on the correction of MIA-associated microglial, synaptic, and neurobehavioral dysfunctions.

Preoperative immune landscape predisposes adverse outcomes in hepatocellular carcinoma patients with liver transplantation

Immune class in hepatocellular carcinoma (HCC) has been shown to possess immunogenic power; however, how preestablished immune landscapes in premalignant and early HCC stages impact the clinical outcomes of HCC patients remains unexplored. We sequenced bulk transcriptomes for 62 malignant tumor samples from a Korean HCC cohort in which 38 patients underwent total hepatectomy, as well as for 15 normal and 47 adjacent nontumor samples. Using in silico deconvolution of expression mixtures, 22 immune cell fractions for each sample were inferred, and validated with immune cell counting by immunohistochemistry. Cell type-specific immune signatures dynamically shifted from premalignant stages to the late HCC stage. Total hepatectomy patients displayed elevated immune infiltration and prolonged disease-free survival compared to the partial hepatectomy patients. However, patients who exhibited an infiltration of regulatory T cells (Tregs) during the pretransplantation period displayed a high risk of tumor relapse with suppressed immune responses, and pretreatment was a potential driver of Treg infiltration in the total hepatectomy group. Treg infiltration appeared to be independent of molecular classifications based on transcriptomic data. Our study provides not only comprehensive immune signatures in adjacent nontumor lesions and early malignant HCC stages but also clinical guidance for HCC patients who will undergo liver transplantation.

Increased longevity due to sexual activity in mole-rats is associated with transcriptional changes in the HPA stress axis

Sexual activity and/or reproduction are associated with a doubling of life expectancy in the long-lived rodent genus Fukomys. To investigate the molecular mechanisms underlying this phenomenon, we analyzed 636 RNA-seq samples across 15 tissues. This analysis suggests that changes in the regulation of the hypothalamic–pituitary–adrenal stress axis play a key role regarding the extended life expectancy of reproductive vs. non-reproductive mole-rats. This is substantiated by a corpus of independent evidence. In accordance with previous studies, the up-regulation of the proteasome and so-called ‘anti-aging molecules’, for example, dehydroepiandrosterone, is linked with enhanced lifespan. On the other hand, several of our results are not consistent with knowledge about aging of short-lived model organisms. For example, we found the up-regulation of the insulin-like growth factor 1/growth hormone axis and several other anabolic processes to be compatible with a considerable lifespan prolongation. These contradictions question the extent to which findings from short-lived species can be transferred to longer-lived ones.

Gene co-expression network analysis reveals immune cell infiltration as a favorable prognostic marker in non-uterine leiomyosarcoma

The present study aimed to improve the understanding of non-uterine leiomyosarcoma (NULMS) prognostic genes through system biology approaches. This cancer is heterogeneous and rare. Moreover, gene interaction networks have not been reported in NULMS yet. The datasets were obtained from the public gene expression databases. Seven co-expression modules were identified from 5000 most connected genes; using weighted gene co-expression network analysis. Using Cox regression, the modules showed favorable (HR = 0.6, 95% CI = 0.4-0.89, P = 0.0125), (HR = 0.65, 95% CI = 0.44-0.98, P = 0.04) and poor (HR = 1.55, 95% CI = 1.06-2.27, P = 0.025) prognosis to the overall survival (OS) (time = 3740 days). The first one was significant in multivariate HR estimates (HR = 0.4, 95% CI = 0.28-0.69, P = 0.0004). Enriched genes through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) revealed significant immune-related pathways; suggesting immune cell infiltration as a favorable prognostic factor. The most significant protective genes were ICAM3, NCR3, KLRB1, and IL18RAP, which were in one of the significant modules. Moreover, genes related to angiogenesis, cell-cell adhesion, protein glycosylation, and protein transport such as PYCR1, SRM, and MDFI negatively affected the OS and were found in the other related module. In conclusion, our analysis suggests that NULMS might be a good candidate for immunotherapy. Moreover, the genes found in this study might be potential candidates for targeted therapy.

BatchServer: A Web Server for Batch Effect Evaluation, Visualization, and Correction

Batch effects are unwanted data variations that may obscure biological signals, leading to bias or errors in subsequent data analyses. Effective evaluation and elimination of batch effects are necessary for omics data analysis. In order to facilitate the evaluation and correction of batch effects, here we present BatchSever, an open-source R/Shiny based user-friendly interactive graphical web platform for batch effects analysis. In BatchServer, we introduced autoComBat, a modified version of ComBat, which is the most widely adopted tool for batch effect correction. BatchServer uses PVCA (Principal Variance Component Analysis) and UMAP (Manifold Approximation and Projection) for evaluation and visualization of batch effects. We demonstrate its applications in multiple proteomics and transcriptomic data sets. BatchServer is provided at https://lifeinfor.shinyapps.io/batchserver/ as a web server. The source codes are freely available at https://github.com/guomics-lab/batch_server.

ComBat-seq: batch effect adjustment for RNA-seq count data

The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.

Transcriptome profiles acquired during cell expansion and licensing validate mesenchymal stromal cell lineage genes

Background

Mesenchymal stromal cells (MSCs) are rapidly advancing as commercial therapeutics. However, there are still no adequate tools to validate the identity of MSCs and support standardization of MSC-based products. Currently accepted metrics include cell surface marker profiling and tri-lineage differentiation assays, neither of which is definitive. Transcript profiling represents a cost- and time-effective approach amenable to MSC manufacturing processes. Two independent labs recently reported non-overlapping MSC-specific transcriptomic signatures of 489 and 16 genes.

Methods

Here, we interrogated our repository of transcriptome data to determine whether routine culture manipulations including cell expansion and immune activation affect expression of the reported MSC lineage genes. These data sets comprise 4 donor populations of human umbilical cord (UC) MSCs serially cultured from cryopreservation thaw through pre-senescence, and 3 donor populations each of naïve UC and bone marrow (BM) MSCs and licensed by 3 different cytokines.

Results

Overall, 437 of 456 proposed signature genes assessed in these data sets were reliably expressed, representing an enduring lineage profile in 96% agreement with the previous studies. Serial passaging resulted in the downregulation of 3 signature genes, and one was silenced. Cytokine stimulation downregulated expression of 16 signature genes, and 3 were uniformly silenced in one or the other MSC type. Fifteen additional genes were unreliably detected, independent of culture manipulation.

Conclusion

These results validate and refine the proposed transcriptomic tools for reliable identification of MSCs after isolation through cell expansion and after inflammatory activation. We propose a 24-gene signature to support standardized and accessible MSC characterization.

Cross-validation of existing signatures and derivation of a novel 29-gene transcriptomic signature predictive of progression to TB in a Brazilian cohort of household contacts of pulmonary TB

The goal of this study was to identify individuals at risk of progression and reactivation among household contacts (HHC) of pulmonary TB cases in Vitoria, Brazil. We first evaluated the predictive performance of six published signatures on the transcriptional dataset obtained from peripheral blood mononuclear cell samples from HHC that either progressed to TB disease or not (non-progressors) during a five-year follow-up. The area under the curve (AUC) values for the six signatures ranged from 0.670 to 0.461, and the PPVs did not reach the WHO published target product profiles (TPPs). We therefore used as training cohort the earliest time-point samples from the African cohort of adolescents (GSE79362) and applied an ensemble feature selection pipeline to derive a novel 29-gene signature (PREDICT29). PREDICT29 was tested on 16 progressors and 21 non-progressors. PREDICT29 performed better in segregating progressors from non-progressors in the Brazil cohort with the area under the curve (AUC) value of 0.911 and PPV of 20%. This proof of concept study demonstrates that PREDICT29 can predict risk of progression/reactivation to clinical TB disease in recently exposed individuals at least 5 years prior to disease development. Upon validation in larger and geographically diverse cohorts, PREDICT29 can be used to risk-stratify recently infected for targeted therapy.

Review of Batch Effects Prevention, Diagnostics, and Correction Approaches

Systematic technical variation in high-throughput studies consisting of the serial measurement of large sample cohorts is known as batch effects. Batch effects reduce the sensitivity of biological signal extraction and can cause significant artifacts. The systematic bias in the data caused by batch effects is more common in studies in which logistical considerations restrict the number of samples that can be prepared or profiled in a single experiment, thus necessitating the arrangement of subsets of study samples in batches. To mitigate the negative impact of batch effects, statistical approaches for batch correction are used at the stage of experimental design and data processing. Whereas in genomics batch effects and possible remedies have been extensively discussed, they are a relatively new challenge in proteomics because methods with sufficient throughput to systematically measure through large sample cohorts have only recently become available. Here we provide general recommendations to mitigate batch effects: we discuss the design of large-scale proteomic studies, review the most commonly used tools for batch effect correction and overview their application in proteomics.

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Objectives

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients’ whole‐blood transcriptomes.

Methods

We applied machine learning approaches to RNA‐sequencing (RNA‐seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta‐analysis on three recently published whole‐blood RNA‐seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed.

Results

Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease‐related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified.

Conclusion

Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

A combined epigenome- and transcriptome-wide association study of the oral masticatory mucosa assigns CYP1B1 a central role for epithelial health in smokers

Background

The oral mucosa has an important role in maintaining barrier integrity at the gateway to the gastrointestinal and respiratory tracts. Smoking is a strong environmental risk factor for the common oral inflammatory disease periodontitis and oral cancer. Cigarette smoke affects gene methylation and expression in various tissues. This is the first epigenome-wide association study (EWAS) that aimed to identify biologically active methylation marks of the oral masticatory mucosa that are associated with smoking.

Results

Ex vivo biopsies of 18 current smokers and 21 never smokers were analysed with the Infinium Methylation EPICBeadChip and combined with whole transcriptome RNA sequencing (RNA-Seq; 16 mio reads per sample) of the same samples. We analysed the associations of CpG methylation values with cigarette smoking and smoke pack year (SPY) levels in an analysis of covariance (ANCOVA). Nine CpGs were significantly associated with smoking status, with three CpGs mapping to the genetic region of CYP1B1 (cytochrome P450 family 1 subfamily B member 1; best p = 5.5 × 10⁻⁸) and two mapping to AHRR (aryl-hydrocarbon receptor repressor; best p = 5.9 × 10⁻⁹). In the SPY analysis, 61 CpG sites at 52 loci showed significant associations of the quantity of smoking with changes in methylation values. Here, the most significant association located to the gene CYP1B1, with p = 4.0 × 10⁻¹⁰. RNA-Seq data showed significantly increased expression of CYP1B1 in smokers compared to non-smokers (p = 2.2 × 10⁻¹⁴), together with 13 significantly upregulated transcripts. Six transcripts were significantly downregulated. No differential expression was observed for AHRR. In vitro studies with gingival fibroblasts showed that cigarette smoke extract directly upregulated the expression of CYP1B1.

Conclusion

This study validated the established role of CYP1B1 and AHRR in xenobiotic metabolism of tobacco smoke and highlights the importance of epigenetic regulation for these genes. For the first time, we give evidence of this role for the oral masticatory mucosa.

Electronic supplementary material

The online version of this article (10.1186/s13148-019-0697-y) contains supplementary material, which is available to authorized users.

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

BACKGROUND

Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking.

RESULTS

We developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components.

CONCLUSION

pcaExplorer is distributed as an R package in the Bioconductor project ( http://bioconductor.org/packages/pcaExplorer/ ), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.

BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm

MOTIVATION

In contemporary biological experiments, bias, which interferes with the measurements, requires attentive processing. Important sources of bias in high-throughput biological experiments are batch effects and diverse methods towards removal of batch effects have been established. These include various normalization techniques, yet many require knowledge on the number of batches and assignment of samples to batches. Only few can deal with the problem of identification of batch effect of unknown structure. For this reason, an original batch identification algorithm through dynamical programming is introduced for omics data that may be sorted on a timescale.

RESULTS

BatchI algorithm is based on partitioning a series of high-throughput experiment samples into sub-series corresponding to estimated batches. The dynamic programming method is used for splitting data with maximal dispersion between batches, while maintaining minimal within batch dispersion. The procedure has been tested on a number of available datasets with and without prior information about batch partitioning. Datasets with a priori identified batches have been split accordingly, measured with weighted average Dice Index. Batch effect correction is justified by higher intra-group correlation. In the blank datasets, identified batch divisions lead to improvement of parameters and quality of biological information, shown by literature study and Information Content. The outcome of the algorithm serves as a starting point for correction methods. It has been demonstrated that omitting the essential step of batch effect control may lead to waste of valuable potential discoveries.

AVAILABILITY AND IMPLEMENTATION

The implementation is available within the BatchI R package at http://zaed.aei.polsl.pl/index.php/pl/111-software.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Accumulating Transcriptome Drift Precedes Cell Aging in Human Umbilical Cord-Derived Mesenchymal Stromal Cells Serially Cultured to Replicative Senescence

In preclinical studies, mesenchymal stromal cells (MSCs) exhibit robust potential for numerous applications. To capitalize on these benefits, cell manufacturing and delivery protocols have been scaled up to facilitate clinical trials without adequately addressing the impact of these processes on cell utility nor inevitable regulatory requirements for consistency. Growing evidence indicates that culture-aged MSCs, expanded to the limits of replicative exhaustion to generate human doses, are not equivalent to early passage cells, and their use may underpin reportedly underwhelming or inconsistent clinical outcomes. Here, we sought to define the maximum expansion boundaries for human umbilical cord-derived MSCs, cultured in chemically defined xeno- and serum-free media, that yield consistent cell batches comparable to early passage cells. Two male and two female donor populations, recovered from cryostorage at mean population doubling level (mPDL) 10, were serially cultivated until replicative exhaustion (senescence). At each passage, growth kinetics, cell morphology, and transcriptome profiles were analyzed. All MSC populations displayed comparable growth trajectories through passage 9 (P9; mPDL 45) and variably approached senescence after P10 (mPDL 49). Transcription profiles of 14,500 human genes, generated by microarray, revealed a nonlinear evolution of culture-adapted MSCs. Significant expression changes occurred only after P5 (mPDL 27) and accumulated rapidly after P9 (mPDL 45), preceding other cell aging metrics. We report that cryobanked umbilical cord-derived MSCs can be reliably expanded to clinical human doses by P4 (mPDL 23), before significant transcriptome drift, and thus represent a mesenchymal cell source suited for clinical translation of cellular therapies. Stem Cells Translational Medicine 2019;8:945&958.

Human umbilical cord perivascular cells: A novel source of the organophosphate antidote butyrylcholinesterase

Human butyrylcholinesterase (BChE) is a well-characterized bioscavenger with significant potential as a prophylactic or post-exposure treatment for organophosphate poisoning. Despite substantial efforts, BChE has proven technically challenging to produce in recombinant systems. Recombinant BChE tends to be insufficiently or incorrectly glycosylated, and consequently exhibits a truncated half-life, compromised activity, or is immunogenic. Thus, expired human plasma remains the only reliable source of the benchmark BChE tetramer, but production is costly and time intensive and presents possible blood-borne disease hazards. Here we report a human BChE production platform that produces functionally active, tetrameric BChE enzyme, without the addition of external factors such as polyproline peptides or chemical or gene modification required by other systems. Human umbilical cord perivascular cells (HUCPVCs) are a rich population of mesenchymal stromal cells (MSCs) derived from Wharton's jelly. We show that HUCPVCs naturally and stably secrete BChE during culture in xeno- and serum-free media, and can be gene-modified to increase BChE output. However, BChE secretion from HUCPVCs is limited by innate feedback mechanisms that can be interrupted by addition of miR 186 oligonucleotide mimics or by competitive inhibition of muscarinic cholinergic signalling receptors by addition of atropine. By contrast, adult bone marrow-derived mesenchymal stromal cells neither secrete measurable levels of BChE naturally, nor after gene modification. Further work is required to fully characterize and disable the intrinsic ceiling of HUCPVC-mediated BChE secretion to achieve commercially relevant enzyme output. However, HUCPVCs present a unique opportunity to produce both native and strategically engineered recombinant BChE enzyme in a human platform with the innate capacity to secrete the benchmark human plasma form.

Alternative empirical Bayes models for adjusting for batch effects in genomic studies

Background

Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies.

Results

Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios.

Conclusions

We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2263-6) contains supplementary material, which is available to authorized users.

Integrative Analysis of Omics Big Data

The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.

Copy Number Variation

Differences between genomes can be due to single nucleotide variants (SNPs), translocations, inversions and copy number variants (CNVs, gain or loss of DNA). The latter can range from sub-microscopic events to complete chromosomal aneuploidies. Small CNVs are often benign but those larger than 250 kb are strongly associated with morbid consequences such as developmental disorders and cancer. Detecting CNVs within and between populations is essential to better understand the plasticity of our genome and to elucidate its possible contribution to disease or phenotypic traits.While the link between SNPs and disease susceptibility has been well studied, to date there are still very few published CNV genome-wide association studies; probably owing to the fact that CNV analysis remains a slightly more complex task than SNP analysis (both in term of bioinformatics workflow and uncertainty in the CNV calling leading to high false positive rates and unknown false negative rates). This chapter aims at explaining computational methods for the analysis of CNVs, ranging from study design, data processing and quality control, up to genome-wide association study with clinical traits.

DBNorm: normalizing high-density oligonucleotide microarray data based on distributions

Background

Data from patients with rare diseases is often produced using different platforms and probe sets because patients are widely distributed in space and time. Aggregating such data requires a method of normalization that makes patient records comparable.

Results

This paper proposed DBNorm, implemented as an R package, is an algorithm that normalizes arbitrarily distributed data to a common, comparable form. Specifically, DBNorm merges data distributions by fitting functions to each of them, and using the probability of each element drawn from the fitted distribution to merge it into a global distribution. DBNorm contains state-of-the-art fitting functions including Polynomial, Fourier and Gaussian distributions, and also allows users to define their own fitting functions if required.

Conclusions

The performance of DBNorm is compared with z-score, average difference, quantile normalization and ComBat on a set of datasets, including several that are publically available. The performance of these normalization methods are compared using statistics, visualization, and classification when class labels are known based on a number of self-generated and public microarray datasets. The experimental results show that DBNorm achieves better normalization results than conventional methods. Finally, the approach has the potential to be applicable outside bioinformatics analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1912-5) contains supplementary material, which is available to authorized users.