DendroX: multi-level multi-cluster selection in dendrograms.

BACKGROUND: Cluster heatmaps are widely used in biology and other fields to uncover clustering patterns in data matrices. Most cluster heatmap packages provide utility functions to divide the dendrograms at a certain level to obtain clusters, but it is often difficult to locate the appropriate cut in the dendrogram to obtain the clusters seen in the heatmap or computed by a statistical method. Multiple cuts are required if the clusters locate at different levels in the dendrogram. RESULTS: We developed DendroX, a web app that provides interactive visualization of a dendrogram where users can divide the dendrogram at any level and in any number of clusters and pass the labels of the identified clusters for functional analysis. Helper functions are provided to extract linkage matrices from cluster heatmap objects in R or Python to serve as input to the app. A graphic user interface was also developed to help prepare input files for DendroX from data matrices stored in delimited text files. The app is scalable and has been tested on dendrograms with tens of thousands of leaf nodes. As a case study, we clustered the gene expression signatures of 297 bioactive chemical compounds in the LINCS L1000 dataset and visualized them in DendroX. Seventeen biologically meaningful clusters were identified based on the structure of the dendrogram and the expression patterns in the heatmap. We found that one of the clusters consisting of mostly naturally occurring compounds is not previously reported and has its members sharing broad anticancer, anti-inflammatory and antioxidant activities. CONCLUSIONS: DendroX solves the problem of matching visually and computationally determined clusters in a cluster heatmap and helps users navigate among different parts of a dendrogram. The identification of a cluster of naturally occurring compounds with shared bioactivities implicates a convergence of biological effects through divergent mechanisms.

Comut-viz: efficiently creating and browsing comutation plots online.

BACKGROUND: Comutation plot is a widely used visualization method to deliver a global view of the mutation landscape of large-scale genomic studies. Current tools for creating comutation plot are either offline packages that require coding or online web servers with varied features. When a package is used, it often requires repetitive runs of code to adjust a single feature that might only be a few clicks in a web app. But web apps mostly have limited capacity for customization and cannot handle very large genomic files. RESULTS: To improve on existing tools, we identified features that are most frequently adjusted in creating a plot and incorporate them in Comut-viz that interactively filters and visualizes mutation data as downloadable plots. It includes colored labels for numeric metadata, a preloaded palette for changing colors and two input boxes for adjusting width and height. It accepts standard mutation annotation format (MAF) files as input and can handle large MAF files with more than 200 k rows. As a front-end only app, Comut-viz guarantees privacy of user data and no latency in the analysis. CONCLUSIONS: Comut-viz is a highly responsive and extensible web app to make comutation plots. It provides customization for frequently adjusted features and accepts large genomic files as input. It is suitable for genomic studies with more than a thousand samples.

Minimal differentiation of classical monocytes as they survey steady-state tissues and transport antigen to lymph nodes.

It is thought that monocytes rapidly differentiate to macrophages or dendritic cells (DCs) upon leaving blood. Here we have shown that Ly-6C⁺ monocytes constitutively trafficked into skin, lung, and lymph nodes (LNs). Entry was unaffected in gnotobiotic mice. Monocytes in resting lung and LN had similar gene expression profiles to blood monocytes but elevated transcripts of a limited number of genes including cyclo-oxygenase-2 (COX-2) and major histocompatibility complex class II (MHCII), induced by monocyte interaction with endothelium. Parabiosis, bromodoxyuridine (BrdU) pulse-chase analysis, and intranasal instillation of tracers indicated that instead of contributing to resident macrophages in the lung, recruited endogenous monocytes acquired antigen for carriage to draining LNs, a function redundant with DCs though differentiation to DCs did not occur. Thus, monocytes can enter steady-state nonlymphoid organs and recirculate to LNs without differentiation to macrophages or DCs, revising a long-held view that monocytes become tissue-resident macrophages by default.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

B-myb is an essential regulator of hematopoietic stem cell and myeloid progenitor cell development.

The B-myb (MYBL2) gene is a member of the MYB family of transcription factors and is involved in cell cycle regulation, DNA replication, and maintenance of genomic integrity. However, its function during adult development and hematopoiesis is unknown. We show here that conditional inactivation of B-myb in vivo results in depletion of the hematopoietic stem cell (HSC) pool, leading to profound reductions in mature lymphoid, erythroid, and myeloid cells. This defect is autonomous to the bone marrow and is first evident in stem cells, which accumulate in the S and G2/M phases. B-myb inactivation also causes defects in the myeloid progenitor compartment, consisting of depletion of common myeloid progenitors but relative sparing of granulocyte-macrophage progenitors. Microarray studies indicate that B-myb-null LSK(+) cells differentially express genes that direct myeloid lineage development and commitment, suggesting that B-myb is a key player in controlling cell fate. Collectively, these studies demonstrate that B-myb is essential for HSC and progenitor maintenance and survival during hematopoiesis.

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

Drug/Cell-line Browser: interactive canvas visualization of cancer drug/cell-line viability assay datasets.

SUMMARY: Recently, several high profile studies collected cell viability data from panels of cancer cell lines treated with many drugs applied at different concentrations. Such drug sensitivity data for cancer cell lines provide suggestive treatments for different types and subtypes of cancer. Visualization of these datasets can reveal patterns that may not be obvious by examining the data without such efforts. Here we introduce Drug/Cell-line Browser (DCB), an online interactive HTML5 data visualization tool for interacting with three of the recently published datasets of cancer cell lines/drug-viability studies. DCB uses clustering and canvas visualization of the drugs and the cell lines, as well as a bar graph that summarizes drug effectiveness for the tissue of origin or the cancer subtypes for single or multiple drugs. DCB can help in understanding drug response patterns and prioritizing drug/cancer cell line interactions by tissue of origin or cancer subtype. AVAILABILITY AND IMPLEMENTATION: DCB is an open source Web-based tool that is freely available at: http://www.maayanlab.net/LINCS/DCB CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The characteristic direction: a geometrical approach to identify differentially expressed genes.

BACKGROUND: Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling. RESULTS: Here we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test. CONCLUSIONS: The application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.

Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics.

BACKGROUND: Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection. METHODS: One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance. RESULTS: The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved. CONCLUSIONS: Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.

BACKGROUND: System-wide profiling of genes and proteins in mammalian cells produce lists of differentially expressed genes/proteins that need to be further analyzed for their collective functions in order to extract new knowledge. Once unbiased lists of genes or proteins are generated from such experiments, these lists are used as input for computing enrichment with existing lists created from prior knowledge organized into gene-set libraries. While many enrichment analysis tools and gene-set libraries databases have been developed, there is still room for improvement. RESULTS: Here, we present Enrichr, an integrative web-based and mobile software application that includes new gene-set libraries, an alternative approach to rank enriched terms, and various interactive visualization approaches to display enrichment results using the JavaScript library, Data Driven Documents (D3). The software can also be embedded into any tool that performs gene list analysis. We applied Enrichr to analyze nine cancer cell lines by comparing their enrichment signatures to the enrichment signatures of matched normal tissues. We observed a common pattern of up regulation of the polycomb group PRC2 and enrichment for the histone mark H3K27me3 in many cancer cell lines, as well as alterations in Toll-like receptor and interlukin signaling in K562 cells when compared with normal myeloid CD33+ cells. Such analyses provide global visualization of critical differences between normal tissues and cancer cell lines but can be applied to many other scenarios. CONCLUSIONS: Enrichr is an easy to use intuitive enrichment analysis web-based tool providing various types of visualization summaries of collective functions of gene lists. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr.

Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis.

We provide a single-cell atlas of idiopathic pulmonary fibrosis (IPF), a fatal interstitial lung disease, by profiling 312,928 cells from 32 IPF, 28 smoker and nonsmoker controls, and 18 chronic obstructive pulmonary disease (COPD) lungs. Among epithelial cells enriched in IPF, we identify a previously unidentified population of aberrant basaloid cells that coexpress basal epithelial, mesenchymal, senescence, and developmental markers and are located at the edge of myofibroblast foci in the IPF lung. Among vascular endothelial cells, we identify an ectopically expanded cell population transcriptomically identical to bronchial restricted vascular endothelial cells in IPF. We confirm the presence of both populations by immunohistochemistry and independent datasets. Among stromal cells, we identify IPF myofibroblasts and invasive fibroblasts with partially overlapping cells in control and COPD lungs. Last, we confirm previous findings of profibrotic macrophage populations in the IPF lung. Our comprehensive catalog reveals the complexity and diversity of aberrant cellular populations in IPF.

Cell-specific prediction and application of drug-induced gene expression profiles.

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling.

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

Activation of tumor suppressor protein PP2A inhibits KRAS-driven tumor growth.

Targeted cancer therapies, which act on specific cancer-associated molecular targets, are predominantly inhibitors of oncogenic kinases. While these drugs have achieved some clinical success, the inactivation of kinase signaling via stimulation of endogenous phosphatases has received minimal attention as an alternative targeted approach. Here, we have demonstrated that activation of the tumor suppressor protein phosphatase 2A (PP2A), a negative regulator of multiple oncogenic signaling proteins, is a promising therapeutic approach for the treatment of cancers. Our group previously developed a series of orally bioavailable small molecule activators of PP2A, termed SMAPs. We now report that SMAP treatment inhibited the growth of KRAS-mutant lung cancers in mouse xenografts and transgenic models. Mechanistically, we found that SMAPs act by binding to the PP2A Aα scaffold subunit to drive conformational changes in PP2A. These results show that PP2A can be activated in cancer cells to inhibit proliferation. Our strategy of reactivating endogenous PP2A may be applicable to the treatment of other diseases and represents an advancement toward the development of small molecule activators of tumor suppressor proteins.

L1000CDS2: LINCS L1000 characteristic direction signatures search engine.

The library of integrated network-based cellular signatures (LINCS) L1000 data set currently comprises of over a million gene expression profiles of chemically perturbed human cell lines. Through unique several intrinsic and extrinsic benchmarking schemes, we demonstrate that processing the L1000 data with the characteristic direction (CD) method significantly improves signal to noise compared with the MODZ method currently used to compute L1000 signatures. The CD processed L1000 signatures are served through a state-of-the-art web-based search engine application called L1000CDS2. The L1000CDS2 search engine provides prioritization of thousands of small-molecule signatures, and their pairwise combinations, predicted to either mimic or reverse an input gene expression signature using two methods. The L1000CDS2 search engine also predicts drug targets for all the small molecules profiled by the L1000 assay that we processed. Targets are predicted by computing the cosine similarity between the L1000 small-molecule signatures and a large collection of signatures extracted from the gene expression omnibus (GEO) for single-gene perturbations in mammalian cells. We applied L1000CDS2 to prioritize small molecules that are predicted to reverse expression in 670 disease signatures also extracted from GEO, and prioritized small molecules that can mimic expression of 22 endogenous ligand signatures profiled by the L1000 assay. As a case study, to further demonstrate the utility of L1000CDS2, we collected expression signatures from human cells infected with Ebola virus at 30, 60 and 120 min. Querying these signatures with L1000CDS2 we identified kenpaullone, a GSK3B/CDK2 inhibitor that we show, in subsequent experiments, has a dose-dependent efficacy in inhibiting Ebola infection in vitro without causing cellular toxicity in human cell lines. In summary, the L1000CDS2 tool can be applied in many biological and biomedical settings, while improving the extraction of knowledge from the LINCS L1000 resource.

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.

Lean Big Data integration in systems biology and systems pharmacology.

Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.

Mutation of pfm affects the adherence of Pseudomonas aeruginosa to host cells and the quorum sensing system.

The Pseudomonas aeruginosa quorum sensing (QS) system is controlled by the signal molecules acyl homoserine lactones (AHLs) that are synthesized from acyl enoyl-acyl carrier proteins (acyl-ACPs) provided by the fatty acid biosynthesis cycle. Pfm (PA2950), an enoyl-CoA reductase, has previously been shown to affect swimming mobility and fatty acid biosynthesis. In this report, we further show that pfm influences bacterial adherence to human cells. Microarray assay results suggest that pfm affects bacterial adherence through its influence on the QS system. Further experiments confirmed that the pfm mutant strain produces significantly less QS signal molecules than the corresponding wild-type strain. Using strains Escherichia coli DH5α(pECP64, lasB'-lacZ) and E. coli DH5α(pECP61.5, rhlA'-lacZ), biosensors for N-(3-oxododecanoyl) homoserine lactone (3O-C(12) -HSL) and N-butyryl homoserine lactone (C(4) -HSL), respectively, we found that pfm mutant strain produces decreased amounts of both signal molecules. Elastase activity and pyocyanin measurements further confirmed the reduced levels of 3O-C(12) -HSL and C(4) -HSL in the pfm mutant. Finally, bacterial virulence, as assessed by the Caenorhabditis elegans worm killing assay, is decreased in the pfm mutant. Taken together, these data indicate that pfm can be an important target for the control of P. aeruginosa infectivity.