23 research outputs found
Coverage statistics for sequence census methods
Background: We study the statistical properties of fragment coverage in
genome sequencing experiments. In an extension of the classic Lander-Waterman
model, we consider the effect of the length distribution of fragments. We also
introduce the notion of the shape of a coverage function, which can be used to
detect abberations in coverage. The probability theory underlying these
problems is essential for constructing models of current high-throughput
sequencing experiments, where both sample preparation protocols and sequencing
technology particulars can affect fragment length distributions.
Results: We show that regardless of fragment length distribution and under
the mild assumption that fragment start sites are Poisson distributed, the
fragments produced in a sequencing experiment can be viewed as resulting from a
two-dimensional spatial Poisson process. We then study the jump skeleton of the
the coverage function, and show that the induced trees are Galton-Watson trees
whose parameters can be computed.
Conclusions: Our results extend standard analyses of shotgun sequencing that
focus on coverage statistics at individual sites, and provide a null model for
detecting deviations from random coverage in high-throughput sequence census
based experiments. By focusing on fragments, we are also led to a new approach
for visualizing sequencing data that should be of independent interest.Comment: 10 pages, 4 figure
Shape-based peak identification for ChIP-Seq
We present a new algorithm for the identification of bound regions from
ChIP-seq experiments. Our method for identifying statistically significant
peaks from read coverage is inspired by the notion of persistence in
topological data analysis and provides a non-parametric approach that is robust
to noise in experiments. Specifically, our method reduces the peak calling
problem to the study of tree-based statistics derived from the data. We
demonstrate the accuracy of our method on existing datasets, and we show that
it can discover previously missed regions and can more clearly discriminate
between multiple binding events. The software T-PIC (Tree shape Peak
Identification for ChIP-Seq) is available at
http://math.berkeley.edu/~vhower/tpic.htmlComment: 12 pages, 6 figure
Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19
IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19.
Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19.
DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022).
INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days.
MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes.
RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively).
CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes.
TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570
Hodge Spaces of Real Toric Varieties
We introduce the notion of a cosheaf on a fan Σ and define the Z2 Hodge spaces Hpq(Σ), which are the homology groups Hp( ∧ q E) of the qth exterior power of the cosheaf E on Σ. Geometrically, for σ ∈ Σ the stalk Eσ of the cosheaf E is the compact real torus in the real orbit Oσ(R) of the real toric variety XΣ(R). The Z2 Hodge spaces Hpq(Σ) are indexed by pairs p,q with 0 ≤ q ≤ p ≤ d, where d = dimΣ. When Σ is a smooth fan, we have Hpq(Σ) = 0 for p � = q. However, for p> q the spaces Hpq(Σ) are not generally well understood. If Σ is the normal fan of a reflexive polytope ∆ then we use polyhedral duality to compute the Z2 Hodge Spaces of Σ. In particular, if the cones of dimension at most k in the face fan Σ ∗ of ∆ are smooth then we compute Hpq(Σ) for p ≤ k − 2. Moreover, if Σ ∗ is a smooth fan then we completely determine the spaces Hpq(Σ). The Z2 Hodge spaces of Σ are related to the topology of both the real and complex points of the toric variety XΣ in the following way: where (E r,d r) is a spectral sequence with Hpq(Σ) = E 1 p,q = E 2 p,q, E 1 p,q Hp(XΣ(R), Z2