A pseudo-value regression approach for differential network analysis of co-expression data.

BACKGROUND: The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a pseudo-value regression approach for network analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information criteria, followed by pseudo-value calculations, which are then entered into a robust regression model. RESULTS: This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus database to identify DC genes that are associated with chronic obstructive pulmonary disease to demonstrate its utility. CONCLUSION: To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.

Gene expression data provide an abundant resource for inferring connections in gene regulatory networks. While methodologies developed for this task have shown success, a challenge remains in comparing the performance among methods. Gold-standard datasets are scarce and limited in use. And while tools for simulating expression data are available, they are not designed to resemble the data obtained from RNA-seq experiments. SeqNet is an R package that provides tools for generating a rich variety of gene network structures and simulating RNA-seq data from them. This produces in silico RNA-seq data for benchmarking and assessing gene network inference methods. The package is available on CRAN and on GitHub at https://github.com/tgrimes/SeqNet.

Executive function improvement in response to meta-cognitive training in chronic mTBI / PTSD.

Objective: We tested Goal Management Training (GMT), which has been recommended as an executive training protocol that may improve the deficits in the complex tasks inherent in life role participation experienced by those with chronic mild traumatic brain injury and post-traumatic stress disease (mTBI/PTSD). We assessed, not only cognitive function, but also life role participation (quality of life). Methods: We enrolled and treated 14 individuals and administered 10 GMT sessions in-person and provided the use of the Veterans Task Manager (VTM), a Smartphone App, which was designed to serve as a "practice-buddy" device to ensure translation of in-person learning to independent home and community practice of complex tasks. Pre-/post-treatment primary measure was the NIH Examiner, Unstructured Task. Secondary measures were as follows: Tower of London time to complete (cTOL), Community Reintegration of Service Members (CRIS) three subdomains [Extent of Participation; Limitations; Satisfaction of Life Role Participation (Satisfaction)]. We analyzed pre-post-treatment, t-test models to explore change, and generated descriptive statistics to inspect given individual patterns of change across measures. Results: There was statistically significant improvement for the NIH EXAMINER Unstructured Task (p < .02; effect size = .67) and cTOL (p < .01; effect size = .52. There was a statistically significant improvement for two CRIS subdomains: Extent of Participation (p < .01; effect size = .75; Limitations (p < .05; effect size = .59). Individuals varied in their treatment response, across measures. Conclusions and Clinical Significance: In Veterans with mTBI/PTSD in response to GMT and the VTM learning support buddy, there was significant improvement in executive cognition processes, sufficiently robust to produce significant improvement in community life role participation. The individual variations support need for precision neurorehabilitation. The positive results occurred in response to treatment advantages afforded by the content of the combined GMT and the employment of the VTM learning support buddy, with advantages including the following: manualized content of the GMT; incremental complex task difficulty; GMT structure and flexibility to incorporate individualized functional goals; and the VTM capability of ensuring translation of in-person instruction to home and community practice, solidifying newly learned executive cognitive processes. Study results support future study, including a potential randomized controlled trial, the manualized GMT and availability of the VTM to ensure future clinical deployment of treatment, as warranted.

Corrigendum: The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies.

[This corrects the article DOI: 10.3389/fimmu.2022.1093242.].

Utility of targeted gene sequencing to differentiate myeloid malignancies from other cytopenic conditions.

The National Heart, Lung, and Blood Institute-funded National MDS Natural History Study (NCT02775383) is a prospective cohort study enrolling patients with cytopenia with suspected myelodysplastic syndromes (MDS) to evaluate factors associated with disease. Here, we sequenced 53 genes in bone marrow samples harvested from 1298 patients diagnosed with myeloid malignancy, including MDS and non-MDS myeloid malignancy or alternative marrow conditions with cytopenia based on concordance between independent histopathologic reviews (local, centralized, and tertiary to adjudicate disagreements when needed). We developed a novel 2-stage diagnostic classifier based on mutational profiles in 18 of 53 sequenced genes that were sufficient to best predict a diagnosis of myeloid malignancy and among those with a predicted myeloid malignancy, predict whether they had MDS. The classifier achieved a positive predictive value (PPV) of 0.84 and negative predictive value (NPV) of 0.8 with an area under the receiver operating characteristic curve (AUROC) of 0.85 when classifying patients as having myeloid vs no myeloid malignancy based on variant allele frequencies (VAFs) in 17 genes and a PPV of 0.71 and NPV of 0.64 with an AUROC of 0.73 when classifying patients as having MDS vs non-MDS malignancy based on VAFs in 10 genes. We next assessed how this approach could complement histopathology to improve diagnostic accuracy. For 99 of 139 (71%) patients (PPV of 0.83 and NPV of 0.65) with local and centralized histopathologic disagreement in myeloid vs no myeloid malignancy, the classifier-predicted diagnosis agreed with the tertiary pathology review (considered the internal gold standard).

The Vacc-SeqQC project: Benchmarking RNA-Seq for clinical vaccine studies.

Introduction: Over the last decade, the field of systems vaccinology has emerged, in which high throughput transcriptomics and other omics assays are used to probe changes of the innate and adaptive immune system in response to vaccination. The goal of this study was to benchmark key technical and analytical parameters of RNA sequencing (RNA-seq) in the context of a multi-site, double-blind randomized vaccine clinical trial. Methods: We collected longitudinal peripheral blood mononuclear cell (PBMC) samples from 10 subjects before and after vaccination with a live attenuated Francisella tularensis vaccine and performed RNA-Seq at two different sites using aliquots from the same sample to generate two replicate datasets (5 time points for 50 samples each). We evaluated the impact of (i) filtering lowly-expressed genes, (ii) using external RNA controls, (iii) fold change and false discovery rate (FDR) filtering, (iv) read length, and (v) sequencing depth on differential expressed genes (DEGs) concordance between replicate datasets. Using synthetic mRNA spike-ins, we developed a method for empirically establishing minimal read-count thresholds for maintaining fold change accuracy on a per-experiment basis. We defined a reference PBMC transcriptome by pooling sequence data and established the impact of sequencing depth and gene filtering on transcriptome representation. Lastly, we modeled statistical power to detect DEGs for a range of sample sizes, effect sizes, and sequencing depths. Results and Discussion: Our results showed that (i) filtering lowly-expressed genes is recommended to improve fold-change accuracy and inter-site agreement, if possible guided by mRNA spike-ins (ii) read length did not have a major impact on DEG detection, (iii) applying fold-change cutoffs for DEG detection reduced inter-set agreement and should be used with caution, if at all, (iv) reduction in sequencing depth had a minimal impact on statistical power but reduced the identifiable fraction of the PBMC transcriptome, (v) after sample size, effect size (i.e. the magnitude of fold change) was the most important driver of statistical power to detect DEG. The results from this study provide RNA sequencing benchmarks and guidelines for planning future similar vaccine studies.

A novel probabilistic generator for large-scale gene association networks.

MOTIVATION: Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators-such as GeneNetWeaver-are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. RESULTS: We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used "scale-free" model is insufficient for replicating these structures. AVAILABILITY: This generator is implemented in the R package "SeqNet" and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).

The Analysis of Gene Expression Data Incorporating Tumor Purity Information.

The tumor microenvironment is composed of tumor cells, stroma cells, immune cells, blood vessels, and other associated non-cancerous cells. Gene expression measurements on tumor samples are an average over cells in the microenvironment. However, research questions often seek answers about tumor cells rather than the surrounding non-tumor tissue. Previous studies have suggested that the tumor purity (TP)-the proportion of tumor cells in a solid tumor sample-has a confounding effect on differential expression (DE) analysis of high vs. low survival groups. We investigate three ways incorporating the TP information in the two statistical methods used for analyzing gene expression data, namely, differential network (DN) analysis and DE analysis. Analysis 1 ignores the TP information completely, Analysis 2 uses a truncated sample by removing the low TP samples, and Analysis 3 uses TP as a covariate in the underlying statistical models. We use three gene expression data sets related to three different cancers from the Cancer Genome Atlas (TCGA) for our investigation. The networks from Analysis 2 have greater amount of differential connectivity in the two networks than that from Analysis 1 in all three cancer datasets. Similarly, Analysis 1 identified more differentially expressed genes than Analysis 2. Results of DN and DE analyses using Analysis 3 were mostly consistent with those of Analysis 1 across three cancers. However, Analysis 3 identified additional cancer-related genes in both DN and DE analyses. Our findings suggest that using TP as a covariate in a linear model is appropriate for DE analysis, but a more robust model is needed for DN analysis. However, because true DN or DE patterns are not known for the empirical datasets, simulated datasets can be used to study the statistical properties of these methods in future studies.

Trajectories of stroke recovery of impairment, function, and quality of life in response to 12-month mobility and fitness intervention.

BACKGROUND: Gait deficits and functional disability are persistent problems for many stroke survivors, even after standard neurorehabilitation. There is little quantified information regarding the trajectories of response to a long-dose, 12-month intervention. OBJECTIVE: We quantified treatment response to an intensive neurorehabilitation mobility and fitness program. METHODS: The 12-month neurorehabilitation program targeted impairments in balance, limb coordination, gait coordination, and functional mobility, for five chronic stroke survivors. We obtained measures of those variables every two months. RESULTS: We found statistically and clinically significant group improvement in measures of impairment and function. There was high variation across individuals in terms of the timing and the gains exhibited. CONCLUSIONS: Long-duration neurorehabilitation (12 months) for mobility/fitness produced clinically and/or statistically significant gains in impairment and function. There was unique pattern of change for each individual. Gains exhibited late in the treatment support a 12-month intervention. Some measures for some subjects did not reach a plateau at 12 months, justifying further investigation of a longer program (>12 months) of rehabilitation and/or maintenance care for stroke survivors.

Innovative Long-Dose Neurorehabilitation for Balance and Mobility in Chronic Stroke: A Preliminary Case Series.

(1) Objective: The objective was two-fold: (a) test a protocol of combined interventions; (b) administer this combined protocol within the framework of a six-month, intensive, long-duration program. The array of interventions was designed to target the treatment-resistant impairments underlying persistent mobility dysfunction: weakness, balance deficit, limb movement dyscoordination, and gait dyscoordination. (2) Methods: A convenience sample of eight chronic stroke survivors (>4 months post stroke) was enrolled. Treatment was 5 days/week, 1-2.5 h/day for 6 months, as follows: strengthening exercise, balance training, limb/gait coordination training, and aerobic exercise. Outcome measures: Berg Balance Scale (BBS), Fugl-Meyer Lower Limb Coordination (FM), gait speed, 6 Minute Walk Test (6MWT), Timed up and Go (TUG), Functional Independence Measure (FIM), Craig Handicap Assessment Rating Tool (CHART), and personal milestones. Pre-/post-treatment comparisons were conducted using the Permutation Test, suitable for ordinal measures and small sample size. (3) Results: For the group, there was a statistically (p ≤ 0.04) significant improvement in balance, limb movement coordination (FM), gait speed, functional mobility (TUG), and functional activities (FIM). There were measurable differences (minimum detectible change: MDC) in BBS, FM, gait speed, 6MWT, and TUG. There were clinically significant milestones achieved for selected subjects according to clinical benchmarks for the BBS, 6MWT, gait speed, and TUG, as well as achievement of personal milestones of life role participation. Effect sizes (Cohen's D) ranged from 0.5 to 1.0 (with the exception of the (6MWT)). After six months of treatment, the above array of gains were beyond that reported by other published studies of chronic stroke survivor interventions. Personal milestones included: walking to mailbox, gardening/yardwork, walking a distance to neighbors, return to driving, membership at a fitness center, vacation trip to the beach, swimming at local pool, returning to work, housework, cooking meals. (4) Conclusions: Stroke survivors with mobility dysfunction were able to participate in the long-duration, intensive program, with the intervention array targeted to address impairments underlying mobility dysfunction. There were either clinically or statistically significant improvements in an array of measures of impairment, functional mobility, and personal milestone achievements.

Integrating gene regulatory pathways into differential network analysis of gene expression data.

The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. Research in systems biology has taken advantage of these opportunities by gleaning insights into gene regulatory networks through the analysis of gene association networks. Contrasting networks from different populations can reveal the many different roles genes fill, which can lead to new discoveries in gene function. Pathologies can also arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis between two populations is proposed. The framework importantly allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differentially connected genes when incorporating known pathways, even if the pathway knowledge is partially inaccurate. Another simulation study compares the general framework with four state-of-the-art methods. Two RNA-seq datasets are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.

Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

BACKGROUND: Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles. RESULTS: The use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets. CONCLUSIONS: Using predicted survival times based on RNA-seq data can provide improved prognosis by subclassifying clinically high-risk neuroblastoma patients. REVIEWERS: This article was reviewed by Subharup Guha and Isabel Nepomuceno.

Unraveling bacterial fingerprints of city subways from microbiome 16S gene profiles.

BACKGROUND: Microbial communities can be location specific, and the abundance of species within locations can influence our ability to determine whether a sample belongs to one city or another. As part of the 2017 CAMDA MetaSUB Inter-City Challenge, next generation sequencing (NGS) data was generated from swipe samples collected from subway stations in Boston, New York City hereafter New York, and Sacramento. DNA was extracted and Illumina sequenced. Sequencing data was provided for all cities as part of 2017 CAMDA contest challenge dataset. RESULTS: Principal component analysis (PCA) showed clear clustering of the samples for the three cities, with a substantial proportion of the variance explained by the first three components. We ran two different classifiers and results were robust for error rate (< 6%) and accuracy (> 95%). The analysis of variance (ANOVA) demonstrated that overall, bacterial composition across the three cities is significantly different. A similar conclusion was reached using a novel bootstrap based test using diversity indices. Last but not least, a co-abundance association network analyses for the taxonomic levels "order", "family", and "genus" found different patterns of bacterial networks for the three cities. CONCLUSIONS: Bacterial fingerprint can be useful to predict sample provenance. In this work prediction of provenance reported with over 95% accuracy. Association based network analysis, emphasized similarities between the closest cities sharing common bacterial composition. ANOVA showed different patterns of bacterial amongst cities, and these findings strongly suggest that bacterial signature across multiple cities are different. This work advocates a data analysis pipeline which could be followed in order to get biological insight from this data. However, the biological conclusions from this analysis is just an early indication out of a pilot microbiome data provided to us through CAMDA 2017 challenge and will be subject to change as we get more complete data sets in the near future. This microbiome data can have potential applications in forensics, ecology, and other sciences. REVIEWERS: This article was reviewed by Klas Udekwu, Alexandra Graf, and Rafal Mostowy.