A novel induced pluripotent stem cell model of schwann cell differentiation reveals NF2 -related gene regulatory networks of the extracellular matrix.

Schwann cells are vital to development and maintenance of the peripheral nervous system and their dysfunction has been implicated in a range of neurological and neoplastic disorders, including NF2 -related schwannomatosis. We have developed a novel human induced pluripotent stem cell (hiPSC) model for the study of Schwann cell differentiation in health and disease. We performed transcriptomic, immunofluorescence, and morphological analysis of hiPSC derived Schwann cell precursors (SPCs) and terminally differentiated Schwann-like cells (SLCs) representing distinct stages of development. To further validate our findings, we performed integrated, cross-species analyses across multiple external datasets at bulk and single cell resolution. Our hiPSC model of Schwann cell development shared overlapping gene expression signatures with human amniotic mesenchymal stem cell (hAMSCs) derived SLCs and in vivo mouse models, but also revealed unique features that may reflect species-specific aspects of Schwann cell biology. Moreover, we have identified gene co-expression modules that are dynamically regulated during hiPSC to SLC differentiation associated with ear and neural development, cell fate determination, the NF2 gene, and extracellular matrix (ECM) organization. By cross-referencing results between multiple datasets and analyses, we have identified potential new genes that are related to NF2 for further study including: ANXA1, CDH6, COL1A1, COL8A1, MFAP5, IGFBP5, FGF1, AHNAK, CDKN2B, LOX, CAV1 , and CAV2 . Our hiPSC model further provides a tractable platform for studying Schwann cell development in the context of human disease.

Ventromedial hypothalamic nucleus subset stimulates tissue thermogenesis via preoptic area outputs.

OBJECTIVE: Hypothalamic signals potently stimulate energy expenditure by engaging peripheral mechanisms to restore energy homeostasis. Previous studies have identified several critical hypothalamic sites (e.g. preoptic area (POA) and ventromedial hypothalamic nucleus (VMN)) that could be part of an interconnected neurocircuit that controls tissue thermogenesis and essential for body weight control. However, the key neurocircuit that can stimulate energy expenditure has not yet been established. METHODS: Here, we investigated the downstream mechanisms by which VMN neurons stimulate adipose tissue thermogenesis. We manipulated subsets of VMN neurons acutely as well as chronically and studied its effect on tissue thermogenesis and body weight control, using Sf1Cre and Adcyap1Cre mice and measured physiological parameters under both high-fat diet and standard chow diet conditions. To determine the node efferent to these VMN neurons, that is involved in modulating energy expenditure, we employed electrophysiology and optogenetics experiments combined with measurements using tissue-implantable temperature microchips. RESULTS: Activation of the VMN neurons that express the steroidogenic factor 1 (Sf1; VMNSf1 neurons) reduced body weight, adiposity and increased energy expenditure in diet-induced obese mice. This function is likely mediated, at least in part, by the release of the pituitary adenylate cyclase-activating polypeptide (PACAP; encoded by the Adcyap1 gene) by the VMN neurons, since we previously demonstrated that PACAP, at the VMN, plays a key role in energy expenditure control. Thus, we then shifted focus to the subpopulation of VMNSf1 neurons that contain the neuropeptide PACAP (VMNPACAP neurons). Since the VMN neurons do not directly project to the peripheral tissues, we traced the location of the VMNPACAP neurons' efferents. We identified that VMNPACAP neurons project to and activate neurons in the caudal regions of the POA whereby these projections stimulate tissue thermogenesis in brown and beige adipose tissue. We demonstrated that selective activation of caudal POA projections from VMNPACAP neurons induces tissue thermogenesis, most potently in negative energy balance and activating these projections lead to some similar, but mostly unique, patterns of gene expression in brown and beige tissue. Finally, we demonstrated that the activation of the VMNPACAP neurons' efferents that lie at the caudal POA are necessary for inducing tissue thermogenesis in brown and beige adipose tissue. CONCLUSIONS: These data indicate that VMNPACAP connections with the caudal POA neurons impact adipose tissue function and are important for induction of tissue thermogenesis. Our data suggests that the VMNPACAP → caudal POA neurocircuit and its components are critical for controlling energy balance by activating energy expenditure and body weight control.

1q amplification and PHF19 expressing high-risk cells are associated with relapsed/refractory multiple myeloma.

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers can be identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and is already studied using RNA-seq. In this study, we generate a large (325,025 cells and 49 patients) single cell multi-omic dataset and jointly quantify ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identify an association between one plasma cell subtype with myeloma progression that we call relapsed/refractory plasma cells (RRPCs). These cells are associated with chromosome 1q alterations, TP53 mutations, and higher expression of PHF19. We also identify downstream regulation of cell cycle inhibitors in these cells, possible regulation by the transcription factor (TF) PBX1 on chromosome 1q, and determine that PHF19 may be acting primarily through this subset of cells.

Identification of type 2 diabetes- and obesity-associated human ß-cells using deep transfer learning.

Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet ß-cells are the sole source of endogenous insulin and our understanding of ß-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in ß-cell function and survival. However, it is difficult to identify which ß-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related ß-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct ß-cell subpopulations. A singular cluster of T2D-associated ß-cells was identified; however, ß-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. DLK1 was heterogeneously expressed among ß-cells and appeared depleted from T2D islets. In conclusion, DEGAS has the potential to advance our holistic understanding of the ß-cell transcriptomic phenotypes, including features that distinguish ß-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.

Enhanced microglial dynamics and paucity of tau seeding in the amyloid plaque microenvironment contributes to cognitive resilience in Alzheimer's disease.

Asymptomatic Alzheimer's disease (AsymAD) describes the status of subjects with preserved cognition but with identifiable Alzheimer's disease (AD) brain pathology (i.e. Aß-amyloid deposits, neuritic plaques, and neurofibrillary tangles) at autopsy. In this study, we investigated the postmortem brains of a cohort of AsymAD cases to gain insight into the underlying mechanisms of resilience to AD pathology and cognitive decline. Our results showed that AsymAD cases exhibit an enrichment of core plaques and decreased filamentous plaque accumulation, as well as an increase in microglia surrounding this last type. In AsymAD cases we found less pathological tau aggregation in dystrophic neurites compared to AD and tau seeding activity comparable to healthy control subjects. We used spatial transcriptomics to further characterize the plaque niche and found autophagy, endocytosis, and phagocytosis within the top upregulated pathways in the AsymAD plaque niche, but not in AD. Furthermore, we found ARP2, an actin-based motility protein crucial to initiate the formation of new actin filaments, increased within microglia in the proximity of amyloid plaques in AsymAD. Our findings support that the amyloid-plaque microenvironment in AsymAD cases is characterized by microglia with highly efficient actin-based cell motility mechanisms and decreased tau seeding compared to AD. These two mechanisms can potentially provide protection against the toxic cascade initiated by Aß that preserves brain health and slows down the progression of AD pathology.

Disease-associated astrocytes and microglia markers are upregulated in mice fed high fat diet.

High-fat diet (HFD) is associated with Alzheimer's disease (AD) and type 2 diabetes risk, which share features such as insulin resistance and amylin deposition. We examined gene expression associated with astrocytes and microglia since dysfunction of these cell types is implicated in AD pathogenesis. We hypothesize gene expression changes in disease-associated astrocytes (DAA), disease-associated microglia and human Alzheimer's microglia exist in diabetic and obese individuals before AD development. By analyzing bulk RNA-sequencing (RNA-seq) data generated from brains of mice fed HFD and humans with AD, 11 overlapping AD-associated differentially expressed genes were identified, including Kcnj2, C4b and Ddr1, which are upregulated in response to both HFD and AD. Analysis of single cell RNA-seq (scRNA-seq) data indicated C4b is astrocyte specific. Spatial transcriptomics (ST) revealed C4b colocalizes with Gfad, a known astrocyte marker, and the colocalization of C4b expressing cells with Gad2 expressing cells, i.e., GABAergic neurons, in mouse brain. There also exists a positive correlation between C4b and Gad2 expression in ST indicating a potential interaction between DAA and GABAergic neurons. These findings provide novel links between the pathogenesis of obesity, diabetes and AD and identify C4b as a potential early marker for AD in obese or diabetic individuals.

Identifying 1q amplification and PHF19 expressing high-risk cells associated with relapsed/refractory multiple myeloma.

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers have been identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and has already been studied using RNA-seq. In this study a massive (325,025 cells and 49 patients) single cell multiomic dataset was generated with jointly quantified ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identified an association between one plasma cell subtype with myeloma progression that we have called relapsed/refractory plasma cells (RRPCs). These cells are associated with 1q alterations, TP53 mutations, and higher expression of PHF19. We also identified downstream regulation of cell cycle inhibitors in these cells, possible regulation of the transcription factor (TF) PBX1 on 1q, and determined that PHF19 may be acting primarily through this subset of cells.

Spatial Transcriptomic Analysis Reveals Associations between Genes and Cellular Topology in Breast and Prostate Cancers.

BACKGROUND: Cancer is the leading cause of death worldwide with breast and prostate cancer the most common among women and men, respectively. Gene expression and image features are independently prognostic of patient survival; but until the advent of spatial transcriptomics (ST), it was not possible to determine how gene expression of cells was tied to their spatial relationships (i.e., topology). METHODS: We identify topology-associated genes (TAGs) that correlate with 700 image topological features (ITFs) in breast and prostate cancer ST samples. Genes and image topological features are independently clustered and correlated with each other. Themes among genes correlated with ITFs are investigated by functional enrichment analysis. RESULTS: Overall, topology-associated genes (TAG) corresponding to extracellular matrix (ECM) and Collagen Type I Trimer gene ontology terms are common to both prostate and breast cancer. In breast cancer specifically, we identify the ZAG-PIP Complex as a TAG. In prostate cancer, we identify distinct TAGs that are enriched for GI dysmotility and the IgA immunoglobulin complex. We identified TAGs in every ST slide regardless of cancer type. CONCLUSIONS: These TAGs are enriched for ontology terms, illustrating the biological relevance to our image topology features and their potential utility in diagnostic and prognostic models.

SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression.

High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).

TSAFinder: exhaustive tumor-specific antigen detection with RNAseq.

MOTIVATION: Tumor-specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides targets for cancer vaccine and adoptive T-cell therapies with curative potential, and TSAs that are highly expressed at the RNA level are more likely to be presented on major histocompatibility complex (MHC)-I. Direct measurements of the RNA expression of peptides would allow for generalized prediction of TSAs. Human leukocyte antigen (HLA)-I genotypes were predicted with seq2HLA. RNA sequencing (RNAseq) fastq files were translated into all possible peptides of length 8-11, and peptides with high and low expressions in the tumor and control samples, respectively, were tested for their MHC-I binding potential with netMHCpan-4.0. RESULTS: A novel pipeline for TSA prediction from RNAseq was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors and validated on matched tumor and control lung adenocarcinoma (LUAD) samples. We show that neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, and a fraction is expressed in matched normal samples. TSAs presented in the proteomics data have higher RNA abundance and lower MHC-I binding percentile, and these attributes are used to discover high confidence TSAs within the validation cohort. Finally, a subset of these high confidence TSAs is expressed in a majority of LUAD tumors and represents attractive vaccine targets. AVAILABILITY AND IMPLEMENTATION: The datasets were derived from sources in the public domain as follows: TSAFinder is open-source software written in python and R. It is licensed under CC-BY-NC-SA and can be downloaded at https://github.com/RNAseqTSA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression. Availability: https://github.com/tsteelejohnson91/DEGAS .

Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images.

BACKGROUND: To help clinicians provide timely treatment and delay disease progression, it is crucial to identify dementia patients during the mild cognitive impairment (MCI) stage and stratify these MCI patients into early and late MCI stages before they progress to Alzheimer's disease (AD). In the process of diagnosing MCI and AD in living patients, brain scans are collected using neuroimaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). These brain scans measure the volume and molecular activity within the brain resulting in a very promising avenue to diagnose patients early in a minimally invasive manner. METHODS: We have developed an optimal transport based transfer learning model to discriminate between early and late MCI. Combing this transfer learning model with bootstrap aggregation strategy, we overcome the overfitting problem and improve model stability and prediction accuracy. RESULTS: With the transfer learning methods that we have developed, we outperform the current state of the art MCI stage classification frameworks and show that it is crucial to leverage Alzheimer's disease and normal control subjects to accurately predict early and late stage cognitive impairment. CONCLUSIONS: Our method is the current state of the art based on benchmark comparisons. This method is a necessary technological stepping stone to widespread clinical usage of MRI-based early detection of AD.

Combinatorial analyses reveal cellular composition changes have different impacts on transcriptomic changes of cell type specific genes in Alzheimer's Disease.

Alzheimer's disease (AD) brains are characterized by progressive neuron loss and gliosis. Previous studies of gene expression using bulk tissue samples often fail to consider changes in cell-type composition when comparing AD versus control, which can lead to differences in expression levels that are not due to transcriptional regulation. We mined five large transcriptomic AD datasets for conserved gene co-expression module, then analyzed differential expression and differential co-expression within the modules between AD samples and controls. We performed cell-type deconvolution analysis to determine whether the observed differential expression was due to changes in cell-type proportions in the samples or to transcriptional regulation. Our findings were validated using four additional datasets. We discovered that the increased expression of microglia modules in the AD samples can be explained by increased microglia proportions in the AD samples. In contrast, decreased expression and perturbed co-expression within neuron modules in the AD samples was likely due in part to altered regulation of neuronal pathways. Several transcription factors that are differentially expressed in AD might account for such altered gene regulation. Similarly, changes in gene expression and co-expression within astrocyte modules could be attributed to combined effects of astrogliosis and astrocyte gene activation. Gene expression in the astrocyte modules was also strongly correlated with clinicopathological biomarkers. Through this work, we demonstrated that combinatorial analysis can delineate the origins of transcriptomic changes in bulk tissue data and shed light on key genes and pathways involved in AD.

Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing.

Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer's brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.

Clinical and Molecular Correlates of Tumor Mutation Burden in Non-Small Cell Lung Cancer.

INTRODUCTION: Recent clinical studies have identified tumor mutation burden (TMB) as a promising therapeutic biomarker of anti-tumor immune checkpoint blockade. However, given the relatively slow turnaround time and high expense in measuring TMB, tobacco smoking history (TSH) is an attractive replacement biomarker. The carcinogenic effects of tobacco smoking may be modified by the protective effects of genome stability genes. This study aims to test the associations between tobacco smoking, genome stability gene inactivation, and TMB. METHODS: Publicly available TSH and DNA somatic alteration data from NSCLC were downloaded from The Cancer Genome Atlas. Correlations and enrichments were calculated with Spearman and Fisher's exact test methods, respectively. Multivariate modeling of TMB was performed with penalized linear regression. RESULTS: 85% of never smokers in adenocarcinomas (LUAD) had low TMB, but a positive TSH was not predictive of hypermutancy. The limited utility of TSH in predicting TMB was reproduced on an independent LUAD dataset. To expand our search for predictors of TMB, we further investigated the contributions of genome stability related genes (GSGs) to TMB. 242/461 (52%) and 300/465 (65%) patients with LUAD and squamous carcinomas (LUSC), respectively, showed evidence of loss of function in at least one of the 182 GSGs. 182 GSGs from 16 pathways were assessed for associations with TMB high tumor status using Fisher's exact test. We performed univariate gene and pathway enrichments in TMB high tumors and found roles forPOLE, REV3L, and FANCE genes, as well as several key GSG pathways. CONCLUSIONS: This study comprehensively tested the association between GSG, tobacco smoking, and TMB in NSCLC. In LUAD, never-smoking status was predictive of low TMB, but overall TSH was not an adequate surrogate biomarker for TMB in NSCLC. Furthermore, we identified an association between GSG inactivation and TMB.

Pseudogene-gene functional networks are prognostic of patient survival in breast cancer.

BACKGROUND: Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival. RESULTS: In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood. CONCLUSIONS: We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions.

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.

BACKGROUND: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients. METHODS: In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances. RESULTS: All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy. CONCLUSIONS: Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.

A protocol to evaluate RNA sequencing normalization methods.

BACKGROUND: RNA sequencing technologies have allowed researchers to gain a better understanding of how the transcriptome affects disease. However, sequencing technologies often unintentionally introduce experimental error into RNA sequencing data. To counteract this, normalization methods are standardly applied with the intent of reducing the non-biologically derived variability inherent in transcriptomic measurements. However, the comparative efficacy of the various normalization techniques has not been tested in a standardized manner. Here we propose tests that evaluate numerous normalization techniques and applied them to a large-scale standard data set. These tests comprise a protocol that allows researchers to measure the amount of non-biological variability which is present in any data set after normalization has been performed, a crucial step to assessing the biological validity of data following normalization. RESULTS: In this study we present two tests to assess the validity of normalization methods applied to a large-scale data set collected for systematic evaluation purposes. We tested various RNASeq normalization procedures and concluded that transcripts per million (TPM) was the best performing normalization method based on its preservation of biological signal as compared to the other methods tested. CONCLUSION: Normalization is of vital importance to accurately interpret the results of genomic and transcriptomic experiments. More work, however, needs to be performed to optimize normalization methods for RNASeq data. The present effort helps pave the way for more systematic evaluations of normalization methods across different platforms. With our proposed schema researchers can evaluate their own or future normalization methods to further improve the field of RNASeq normalization.

BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes.

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression.

Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression.