3D computational reconstruction of tissues with hollow spherical morphologies using single-cell gene expression data.
2015; 10 (3): 459-474
Ranking adverse drug reactions with crowdsourcing.
Journal of medical Internet research
2015; 17 (3)
Single-cell gene expression analysis has contributed to a better understanding of the transcriptional heterogeneity in a variety of model systems, including those used in research in developmental, cancer and stem cell biology. Nowadays, technological advances facilitate the generation of large gene expression data sets in high-throughput format. Strategies are needed to pertinently visualize this information in a tissue structure-related context, so as to improve data analysis and aid the drawing of meaningful conclusions. Here we describe an approach that uses spatial properties of the tissue source to enable the reconstruction of hollow sphere-shaped tissues and organs from single-cell gene expression data in 3D space. To demonstrate our method, we used cells of the mouse otocyst and the renal vesicle as examples. This protocol presents a straightforward computational expression analysis workflow, and it is implemented on the MATLAB and R statistical computing and graphics software platforms. Hands-on time for typical experiments can be <1 h using a standard desktop PC or Mac.
View details for DOI 10.1038/nprot.2015.022
View details for PubMedID 25675210
Integrating Systems Biology Sources Illuminates Drug Action
CLINICAL PHARMACOLOGY & THERAPEUTICS
2014; 95 (6): 663-669
There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events.The intent of the study was to rank ADRs according to severity.We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs.There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy.ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.
View details for DOI 10.2196/jmir.3962
View details for PubMedID 25800813
Reconstruction of the Mouse Otocyst and Early Neuroblast Lineage at Single-Cell Resolution
2014; 157 (4): 964-978
There are significant gaps in our understanding of the pathways by which drugs act. This incomplete knowledge limits our ability to use mechanistic molecular information rationally to repurpose drugs, understand their side effects, and predict their interactions with other drugs. Here, we present DrugRouter, a novel method for generating drug-specific pathways of action by linking target genes, disease genes, and pharmacogenes using gene interaction networks. We construct pathways for more than a hundred drugs and show that the genes included in our pathways (i) co-occur with the query drug in the literature, (ii) significantly overlap or are adjacent to known drug-response pathways, and (iii) are adjacent to genes that are hits in genome-wide association studies assessing drug response. Finally, these computed pathways suggest novel drug-repositioning opportunities (e.g., statins for follicular thyroid cancer), gene-side effect associations, and gene-drug interactions. Thus, DrugRouter generates hypotheses about drug actions using systems biology data.
View details for DOI 10.1038/clpt.2014.51
View details for Web of Science ID 000336415300030
View details for PubMedID 24577151
A method for inferring medical diagnoses from patient similarities.
2013; 11: 194-?
The otocyst harbors progenitors for most cell types of the mature inner ear. Developmental lineage analyses and gene expression studies suggest that distinct progenitor populations are compartmentalized to discrete axial domains in the early otocyst. Here, we conducted highly parallel quantitative RT-PCR measurements on 382 individual cells from the developing otocyst and neuroblast lineages to assay 96 genes representing established otic markers, signaling-pathway-associated transcripts, and novel otic-specific genes. By applying multivariate cluster, principal component, and network analyses to the data matrix, we were able to readily distinguish the delaminating neuroblasts and to describe progressive states of gene expression in this population at single-cell resolution. It further established a three-dimensional model of the otocyst in which each individual cell can be precisely mapped into spatial expression domains. Our bioinformatic modeling revealed spatial dynamics of different signaling pathways active during early neuroblast development and prosensory domain specification. PAPERFLICK:
View details for DOI 10.1016/j.cell.2014.03.036
View details for Web of Science ID 000335765500022
Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools.Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses.Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses.Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.
View details for DOI 10.1186/1741-7015-11-194
View details for PubMedID 24004670