Biology subjects

Hicks, S. C.

Publications and source records attributed to Hicks, S. C..

4 recordsLinked to original sources

bioRxiv2017

Technology-independent estimation of cell type composition using differentially methylated regions

BackgroundHigh-resolution genome-wide measurement of DNA methylation (DNAm) has become a widely used assay in biomedical research. A major challenge in measuring DNAm is variability introduced from intra-sample cellular heterogeneity, which is a convolution of DNAm profiles across cell types. When this source of variability is confounded with an outcome of interest, if unaccounted for, false positives ensue. This is particularly problematic in epigenome-wide association studies for human disease performed on whole blood, a heterogeneous tissue. To account for this source of variability, a first step is to determine the actual cell proportions of each sample. Currently, the most effective approach is based on fitting a linear model in which one assumes the DNAm profiles of the representative cell types are known. However, we can only make this assumption when a dedicated experiment is performed to provide a plug-in estimate for these profiles. Although this method works well in practice, technology-specific biases lead to platform-dependent plug-in profiles. As a result, to apply the current methods across technologies we are required to repeat these costly experiments for each platform.\n\nResultsHere, we present a method that accurately estimates cell proportions agnostic to platform by first using experimental data to identify regions in which each cell type is clearly methylated or unmethlyated and model these as latent states. While the continuous measurements used in the linear model approaches are affected by platform-specific biases, the latent states are biologically driven and therefore technology independent, implying that experimental data only needs to be collected once. We demonstrate that our method accurately estimates the cell composition from whole blood samples and is applicable across multiple platforms, including microarray and sequencing platforms.

genomics↗

bioRxiv2017

Varying-Censoring Aware Matrix Factorization for Single Cell RNA-Sequencing

Single cell RNA-Seq (scRNA-Seq) has become the most widely used high-throughput technology for gene expression profiling of individual cells. The potential of being able to measure cell-to-cell variability at a high-dimensional genomic scale opens numerous new lines of investigation in basic and clinical research. For example, by identifying groups of cells with expression profiles unlike those observed in cells with known phenotypes, new cell types may be discovered. Dimension reduction followed by unsupervised clustering are the quantitative approaches typically used to facilitate such discoveries. However, a challenge for this approach is that most scRNA-Seq datasets are sparse, with the percentages of measurements reported as zero ranging from 35% to 99% across cells, and these zeros are partially explained by experimental inefficiencies that lead to censored data. Furthermore, the observed across-cell differences in the percentages of zeros are partly due to technical artifacts rather than biological differences. Unfortunately, standard dimension reduction approaches treat these censored values as true zeros, which leads to the identification of distorted low-dimensional factors. When these factors are used for clustering, the distortion leads to incorrect identification of biological groups. Here, we propose an approach that accounts for cell-specific censoring with a varying-censoring aware matrix factorization (VAMF) model that permits the identification of factors in the presence of the above described systematic bias. We demonstrate the advantages of our approach on published scRNA-Seq data and confirm these on simulated data.

genomics↗

bioRxiv2017

Analysis And Correction Of Compositional Bias In Sparse Sequencing Count Data

Count data derived from high-throughput DNA sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.

genomics↗

bioRxiv2016

Smooth Quantile Normalization

Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.

genomics↗