Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing.

Functional MRI (fMRI) data may be contaminated by artifacts arising from a myriad of sources, including subject head motion, respiration, heartbeat, scanner drift, and thermal noise. These artifacts cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of the data-all of which can have negative consequences for the accuracy and power of downstream statistical analysis. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts and generally comes in two flavors. Motion scrubbing based on subject head motion-derived measures is popular but suffers from a number of drawbacks, among them the need to choose a threshold, a lack of generalizability to multiband acquisitions, and high rates of censoring of individual volumes and entire subjects. Alternatively, data-driven scrubbing methods like DVARS are based on observed noise in the processed fMRI timeseries and may avoid some of these issues. Here we propose "projection scrubbing", a novel data-driven scrubbing method based on a statistical outlier detection framework and strategic dimension reduction, including independent component analysis (ICA), to isolate artifactual variation. We undertake a comprehensive comparison of motion scrubbing with data-driven projection scrubbing and DVARS. We argue that an appropriate metric for the success of scrubbing is maximal data retention subject to reasonable performance on typical benchmarks such as the validity, reliability, and identifiability of functional connectivity. We find that stringent motion scrubbing yields worsened validity, worsened reliability, and produced small improvements to fingerprinting. Meanwhile, data-driven scrubbing methods tend to yield greater improvements to fingerprinting while not generally worsening validity or reliability. Importantly, however, data-driven scrubbing excludes a fraction of the number of volumes or entire sessions compared to motion scrubbing. The ability of data-driven fMRI scrubbing to improve data retention without negatively impacting the quality of downstream analysis has major implications for sample sizes in population neuroscience research.

Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons. Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd most accurate model measured by WIS in 2021-22 and the 5th most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change. Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.

Sufficient principal component regression for pattern discovery in transcriptomic data.

Motivation: Methods for the global measurement of transcript abundance such as microarrays and RNA-Seq generate datasets in which the number of measured features far exceeds the number of observations. Extracting biologically meaningful and experimentally tractable insights from such data therefore requires high-dimensional prediction. Existing sparse linear approaches to this challenge have been stunningly successful, but some important issues remain. These methods can fail to select the correct features, predict poorly relative to non-sparse alternatives or ignore any unknown grouping structures for the features. Results: We propose a method called SuffPCR that yields improved predictions in high-dimensional tasks including regression and classification, especially in the typical context of omics with correlated features. SuffPCR first estimates sparse principal components and then estimates a linear model on the recovered subspace. Because the estimated subspace is sparse in the features, the resulting predictions will depend on only a small subset of genes. SuffPCR works well on a variety of simulated and experimental transcriptomic data, performing nearly optimally when the model assumptions are satisfied. We also demonstrate near-optimal theoretical guarantees. Availability and implementation: Code and raw data are freely available at https://github.com/dajmcdon/suffpcr. Package documentation may be viewed at https://dajmcdon.github.io/suffpcr. Contact: daniel@stat.ubc.ca. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators-derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity-from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR.

Heterogeneity in transcription initiation has important consequences for transcript stability and translation, and shifts in transcription start site (TSS) usage are prevalent in various developmental, metabolic, and disease contexts. Accordingly, numerous methods for global TSS profiling have been developed, including most recently Survey of TRanscription Initiation at Promoter Elements with high-throughput sequencing (STRIPE-seq), a method to profile transcription start sites (TSSs) on a genome-wide scale with significant cost and time savings compared to previous methods. In anticipation of more widespread adoption of STRIPE-seq and related methods for construction of promoter atlases and studies of differential gene expression, we built TSRexploreR, an R package for end-to-end analysis of TSS mapping data. TSRexploreR provides functions for TSS and transcription start region (TSR) detection, normalization, correlation, visualization, and differential TSS/TSR analyses. TSRexploreR is highly interoperable, accepting the data structures of TSS and TSR sets generated by several existing tools for processing and alignment of TSS mapping data, such as CAGEr for Cap Analysis of Gene Expression (CAGE) data. Lastly, TSRexploreR implements a novel approach for the detection of shifts in TSS distribution.

Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression.

MOTIVATION: The discovery of relationships between gene expression measurements and phenotypic responses is hampered by both computational and statistical impediments. Conventional statistical methods are less than ideal because they either fail to select relevant genes, predict poorly, ignore the unknown interaction structure between genes, or are computationally intractable. Thus, the creation of new methods which can handle many expression measurements on relatively small numbers of patients while also uncovering gene-gene relationships and predicting well is desirable. RESULTS: We develop a new technique for using the marginal relationship between gene expression measurements and patient survival outcomes to identify a small subset of genes which appear highly relevant for predicting survival, produce a low-dimensional embedding based on this small subset, and amplify this embedding with information from the remaining genes. We motivate our methodology by using gene expression measurements to predict survival time for patients with diffuse large B-cell lymphoma, illustrate the behavior of our methodology on carefully constructed synthetic examples, and test it on a number of other gene expression datasets. Our technique is computationally tractable, generally outperforms other methods, is extensible to other phenotypes, and also identifies different genes (relative to existing methods) for possible future study. AVAILABILITY AND IMPLEMENTATION: All of the code and data are available at http://mypage.iu.edu/∼dajmcdon/research/ . CONTACT: dajmcdon@indiana.edu. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Estimating beta-mixing coefficients.

The literature on statistical learning for time series assumes the asymptotic independence or "mixing" of the data-generating process. These mixing assumptions are never tested, and there are no methods for estimating mixing rates from data. We give an estimator for the beta-mixing rate based on a single stationary sample path and show it is L1-risk consistent.

The effect of low intensity ultraviolet-C light on monoclonal antibodies.

As part of an investigation to identify potential new viral reduction strategies, ultraviolet-C (UV-C) light was examined. Although this technology has been known for decades to possess excellent virus inactivation capabilities, UV-C light can also introduce significant unwanted damage to proteins. To study the effect on monoclonal antibodies, three different antibodies were subjected to varying levels of UV-C light using a novel dosing device from Bayer Technology Services GmbH. The range of fluencies (or doses) covered was between 0 and 300 J/m(2) at a wavelength of 254 nm. Product quality data generated from the processed pools showed only minimal damage done to the antibodies. Aggregate formation was low for two of the three antibodies tested. Acidic and basic variants increased for all three antibodies, with the basic species increasing more than the acidic species. Peptide maps made for the three sets of pools showed no damage to two of the three antibody backbones, whereas the third antibody had very low levels of methionine oxidation evident. Samples held at 2-8 degrees C for 33 days showed no increase in aggregates or charge variants, indicating that the proteins did not degrade and were not damaged further by reactive or catalytic species that may have been created on exposure to UV-C light. Overall, UV-C light was shown to induce very little damage to monoclonal antibodies at lower fluencies and appears to be a viable option for viral inactivation in biotechnology applications.