Ben Galili

We propose a method for comparing survival data based on higher criticism of $ p $-values obtained from many exact hypergeometric tests. The method accommodates noninformative right-censorship and is sensitive to hazard differences in unknown and relatively rare time intervals. It attains much better power against such differences than the log-rank test and its variants. We demonstrate the usefulness of our method in detecting rare and weak non-proportional hazard differences compared to existing tests using simulations and gene expression data. Additionally, we analyse the asymptotic power of our method and other tests under a theoretical framework describing two groups experiencing failure rates that are usually identical over time, except in a few unknown instances where one group’s failure rate is higher. Our test’s power experiences a phase transition across the plane of rarity and intensity parameters that mirrors the phase transition of higher criticism in two-sample rare and weak normal and Poisson means settings. The region of the plane in which our method has asymptotically full power is larger than the corresponding region for the log-rank test.

This study introduces a novel model for analyzing and determining the required sequencing coverage in DNA-based data storage, focusing on combinatorial DNA encoding. We seek to characterize the distribution of the number of sequencing reads required for message reconstruction. We use a variant of the coupon collector distribution for this purpose. For any given number of observed reads, R ϵ N, we use a Markov Chain representation of the process to compute the probability of error-free reconstruction. We develop theoretical bounds on the decoding probability and use empirical simulations to validate these bounds and assess tightness. This work contributes to understanding sequencing coverage in DNA-based data storage, offering insights into decoding complexity, error correction, and sequence reconstruction. We provide a Python package, with its input being the code design and other message parameters, all of which are denoted as Θ, and a desired confidence level 1-δ. This package computes the required read coverage, guaranteeing the message reconstruction R=R(δ,Θ).

With the development of sequencing technologies, viral strain classification - which is critical for many applications, including disease monitoring and control - has become widely deployed. Typically, a lab (client) holds a viral sequence, and requests classification services from a centralized repository of labeled viral sequences (server). However, such “classification as a service” raises privacy concerns. In this paper we propose a privacy-preserving viral strain classification protocol that allows the client to obtain classification services from the server, while maintaining complete privacy of the client's viral strains. The privacy guarantee is against active servers, and the correctness guarantee is against passive ones. We implemented our protocol and performed extensive benchmarks, showing that it obtains almost perfect accuracy (99.8%-100%) and microAUC (0.999), and high efficiency (amortized per-sequence client and server runtimes of 4.95ms and 0.53ms, respectively, and 0.21MB communication). In addition, we present an extension of our protocol that guarantees server privacy against passive clients, and provide an empirical evaluation showing that this extension provides the same high accuracy and microAUC, with amortized per sequences overhead of only a few milliseconds in client and server runtime, and 0.3MB in communication complexity. Along the way, we develop an enhanced packing technique in which two reals are packed in a single complex number, with support for homomorphic inner products of vectors of ciphertexts. We note that while similar packing techniques were used before, they only supported additions and multiplication by constants.

Bioinformatics (2021), https://dx.doi.org/10.1093/bioinformatics/btab495

Log-rank test is a widely used test that serves to assess the statistical significance of observed differences in survival, when comparing two or more groups. The log-rank test is based on several assumptions that support the validity of the calculations. It is naturally assumed, implicitly, that no errors occur in the labeling of the samples. That is, the mapping between samples and groups is perfectly correct. In this work, we investigate how test results may be affected when considering some errors in the original labeling. We introduce and define the uncertainty that arises from labeling errors in log-rank test. In order to deal with this uncertainty, we develop a novel algorithm for efficiently calculating a stability interval around the original log-rank P-value and prove its correctness. We demonstrate our algorithm on several datasets. We provide a Python implementation, called LoRSI, for calculating the stability interval using our algorithm https://github.com/YakhiniGroup/LoRSI. Supplementary data are available at Bioinformatics online.

Motivation and background: The patient’s immune system plays an important role in cancer pathogenesis, prognosis and susceptibility to treatment. Recent work introduced an immune related breast cancer. This subtyping is based on the expression profiles of the tumor samples. Specifically, one study showed that analyzing 658 genes can lead to a signature for subtyping tumors. Furthermore, this classification is independent of other known molecular and clinical breast cancer subtyping. Finally, that study shows that the suggested subtyping has significant prognostic implications.Results: In this work we develop an efficient signature associated with survival in breast cancer. We begin by developing a more efficient signature for the above-mentioned breast cancer immune-based subtyping. This signature represents better performance with a set of 579 genes that obtains an improved Area Under Curve (AUC). We then determine a set of 193 genes and an associated classification rule that yield subtypes with a much stronger statistically significant (log rank p-value < 2 × 10⁻⁴in a test cohort) difference in survival. To obtain these improved results we develop a feature selection process that matches the high-dimensionality character of the data and the dual performance objectives, driven by survival and anchored by the literature subtyping.