Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality ...reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.
We present single-cell interpretation via multikernel learning (SIMLR), an analytic framework and software which learns a similarity measure from single-cell RNA-seq data in order to perform ...dimension reduction, clustering and visualization. On seven published data sets, we benchmark SIMLR against state-of-the-art methods. We show that SIMLR is scalable and greatly enhances clustering performance while improving the visualization and interpretability of single-cell sequencing data.
The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread ...of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
. Here we introduce a metapopulation susceptible-exposed-infectious-removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of 'superspreader' points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups
solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19.
Ethical Machine Learning in Healthcare Chen, Irene Y; Pierson, Emma; Rose, Sherri ...
Annual review of biomedical data science,
07/2021, Letnik:
4
Journal Article
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The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML ...in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity ...for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.
How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,000 submitting ...authors and 9,000 submitted papers, we surveyed the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews. The salient results are: (1) Authors had roughly a three-fold overestimate of the acceptance probability of their papers: The median prediction was 70% for an approximately 25% acceptance rate. (2) Female authors exhibited a marginally higher (statistically significant) miscalibration than male authors; predictions of authors invited to serve as meta-reviewers or reviewers were similarly calibrated, but better than authors who were not invited to review. (3) Authors' relative ranking of scientific contribution of two submissions they made generally agreed with their predicted acceptance probabilities (93% agreement), but there was a notable 7% responses where authors predicted a worse outcome for their better paper. (4) The author-provided rankings disagreed with the peer-review decisions about a third of the time; when co-authors ranked their jointly authored papers, co-authors disagreed at a similar rate-about a third of the time. (5) At least 30% of respondents of both accepted and rejected papers said that their perception of their own paper improved after the review process. The stakeholders in peer review should take these findings into account in setting their expectations from peer review.
Classical decision theory predicts that people should be indifferent to information that is not useful for making decisions, but this model often fails to describe human behavior. Here we investigate ...one such scenario, where people desire information about whether an event (the gain/loss of money) will occur even though there is no obvious decision to be made on the basis of this information. We find a curious dual trend: if information is costless, as the probability of the event increases people want the information more; if information is not costless, people's desire for the information peaks at an intermediate probability. People also want information more as the importance of the event increases, and less as the cost of the information increases. We propose a model that explains these results, based on the assumption that people have limited cognitive resources and obtain information about which events will occur so they can determine whether to expend effort planning for them.
Tumor-infiltrating lymphocytes (TIL) in pretreatment biopsies are associated with improved survival in triple-negative breast cancer (TNBC). We investigated whether higher peripheral lymphocyte ...counts are associated with lower breast cancer-specific mortality (BCM) and overall mortality (OM) in TNBC.
Data on treatments and diagnostic tests from electronic medical records of two health care systems were linked with demographic, clinical, pathologic, and mortality data from the California Cancer Registry. Multivariable regression models adjusted for age, race/ethnicity, socioeconomic status, cancer stage, grade, neoadjuvant/adjuvant chemotherapy use, radiotherapy use, and germline
mutations were used to evaluate associations between absolute lymphocyte count (ALC), BCM, and OM. For a subgroup with TIL data available, we explored the relationship between TILs and peripheral lymphocyte counts.
A total of 1,463 stage I-III TNBC patients were diagnosed from 2000 to 2014; 1,113 (76%) received neoadjuvant/adjuvant chemotherapy within 1 year of diagnosis. Of 759 patients with available ALC data, 481 (63.4%) were ever lymphopenic (minimum ALC <1.0 K/μL). On multivariable analysis, higher minimum ALC, but not absolute neutrophil count, predicted lower OM HR = 0.23; 95% confidence interval (CI), 0.16-0.35 and BCM (HR = 0.19; CI, 0.11-0.34). Five-year probability of BCM was 15% for patients who were ever lymphopenic versus 4% for those who were not. An exploratory analysis (
= 70) showed a significant association between TILs and higher peripheral lymphocyte counts during neoadjuvant chemotherapy.
Higher peripheral lymphocyte counts predicted lower mortality from early-stage, potentially curable TNBC, suggesting that immune function may enhance the effectiveness of early TNBC treatment.
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Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: ...varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms—a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test ( P -value: <0.001) and area under the receiving operating characteristic curve among Black participants ( P -value: 0.006). Because the race-blind algorithm underpredicted risk for Black participants, the race-adjusted algorithm increased the fraction of Black participants among the predicted high-risk group, potentially increasing access to screening. More broadly, this study shows that race adjustments may be beneficial when the data quality of key predictors in clinical algorithms differs by race group.
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