gilgi.org (Posts about genomics)

Identifying compartments in Hi-C data

gilgi — Mon, 13 Apr 2020 04:00:00 GMT

Long-range chromatin contact patterns reveal that chromatin is partitioned into distinct "compartments" - contiguous stretches of chromatin on the order of megabases in size whose epigenetic modifications and associated protein factors cause them to be positioned close to similarly-modified compartments in three-dimensional space within the nucleus. In this post, we will take a look at one line of approaches for computationally identifying compartments in Hi-C datasets - one that uses the trans contact profile for each locus in the genome as a feature vector.

Optimal BH-FDR control

gilgi — Fri, 15 Nov 2019 05:00:00 GMT

Controlling for false discovery rate (FDR) has become a ubiquitous step in statistical analyses that test multiple hypotheses, like those commonly performed in the genomics field. The seminal method for controlling FDR, the Benjamini-Hochberg (BH-FDR) procedure guarantees FDR control under some simple assumptions, but how does it really perform in the limit of an ideal dataset and ideal statistical test? In this post, we'll dive into this question and reveal that the proportion of truly non-null data points has a major influence on the behavior of BH-FDR.

Estimation variance of the Poisson rate parameter

gilgi — Tue, 13 Aug 2019 04:00:00 GMT

Many applications of statistics to genomics involve modelling read counts from high-throughput sequencing experiments as originating from a Poisson process (or as something similar based on the closely-related negative binomial distribution). In these experiments, we often want to estimate the true concentration $\mu$ of a particular DNA or RNA sequence in a sequencing library. This corresponds to the "rate" parameter of a Poison process, while the total number of reads sequenced can be thought of as related to its "elapsed time". How does the sequencing depth of the dataset (the total number of reads) impact the variance in the estimate of the true concentration? Read on to find out!

MLE estimation of mean parameter for scaled distributions

gilgi — Wed, 08 May 2019 04:00:00 GMT

It's pretty common to run into statistical models that fit some kind of normalization factors that can be used to scale different data points to a common, comparable scale. When this happens to a location-scale family distribution like the normal distribution, estimating the mean parameter of such a scaled distribution is pretty straightforward. In many genomics contexts, however, non-location-scale distributions such as the Poisson or negative binomial (NB) distribution are used quite often. For example, it's common to model RNA-seq read counts using NB distributions scaled by the library size (i.e., some measure of the total sequencing depth). In this post, we'll take a deep dive into how to estimate the parameters of these "scaled" statistical models.

Simulating maps of 3D genome architecture

gilgi — Mon, 29 Apr 2019 04:00:00 GMT

My research focuses on analyzing maps of the 3D structure of the genome. One of the things I always find helpful when trying to understand complex data like this is trying to simulate it myself to get a sense for how the data might behave. In this post, we'll add complexity step-by-step and work our way up to simulating realistic-looking genome folding maps from scratch!