Impact of data resolution on three-dimensional structure inference methods

Abstract

BackgroundAssays that are capable of detecting genome-wide chromatin interactions have produced massive amount of data and led to great understanding of the chromosomal three-dimensional (3D) structure. As technology becomes more sophisticated, higher-and-higher resolution data are being produced, going from the initial 1 Megabases (Mb) resolution to the current 10 Kilobases (Kb) or even 1 Kb resolution. The availability of genome-wide interaction data necessitates development of analytical methods to recover the underlying 3D spatial chromatin structure, but challenges abound. Most of the methods were proposed for analyzing data at low resolution (1 Mb). Their behaviors are thus unknown for higher resolution data. For such data, one of the key features is the high proportion of “0” contact counts among all available data, in other words, the excess of zeros.ResultsTo address the issue of excess of zeros, in this paper, we propose a truncated Random effect EXpression (tREX) method that can handle data at various resolutions. We then assess the performance of tREX and a number of leading existing methods for recovering the underlying chromatin 3D structure. This was accomplished by creating in-silico data to mimic multiple levels of resolution and submit the methods to a “stress test”. Finally, we applied tREX and the comparison methods to a Hi-C dataset for which FISH measurements are available to evaluate estimation accuracy.ConclusionThe proposed tREX method achieves consistently good performance in all 30 simulated settings considered. It is not only robust to resolution level and underlying parameters, but also insensitive to model misspecification. This conclusion is based on observations made in terms of 3D structure estimation accuracy and preservation of topologically associated domains. Application of the methods to the human lymphoblastoid cell line data on chromosomes 14 and 22 further substantiates the superior performance of tREX: the constructed 3D structure from tREX is consistent with the FISH measurements, and the corresponding distances predicted by tREX have higher correlation with the FISH measurements than any of the comparison methods.SoftwareAn open-source R-package is available at http://www.stat.osu.edu/~statgen/Software/tRex.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0894-z) contains supplementary material, which is available to authorized users.

3D modeling of chromatin structure: is there a way to integrate and reconcile single cell and population experimental data?

“…In all cases, data quality is an important parameter to take into account when choosing for the suitable modeling strategy . In particular, a recent work has shown the possibility to assess the accuracy of resulting 3D models by using statistical properties of the input interaction matrices .…”

“…(1) Bolzer et al 1 (2) Kalhor et al 14 [2], 14,23,44,45 [3], [46][47][48][49][50] [4], [51][52][53][54][55] [5], 44,56-63 and [6]. 14,23,45,64,65 Refer to the text for detailed explanation of each strategy.…”

Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data

“…For examples, methods have been developed to compare models of the same resolution [147]. A recent in-silico study has also been carried out to evaluate methods using simulated data with various resolutions [148]. …”

“…Recognizing the excess of zeroes for higher resolution data such as the in-situ Hi-C data [121], the truncated Poisson Architecture Model (tPAM) and the truncated Random effect EXpression model (tREX) were proposed based on a truncated Poisson distribution [148, 151]. These methods were shown to be robust when the data contain more zeros than expected under a Poisson model, yet the methods were also shown to be efficient when the data are indeed coming from a Poisson distribution.…”

“…These methods were shown to be robust when the data contain more zeros than expected under a Poisson model, yet the methods were also shown to be efficient when the data are indeed coming from a Poisson distribution. Their performances have been compared to optimization-based methods for handing data of various resolutions [148]. …”