Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userBOOST-Ising: Bayesian Modeling of Spatial Transcriptomics Data via Ising Model
83
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Recent technology breakthrough in spatial molecular profiling has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in analysis of spatial molecular profiling data is how to identify spatially variable genes. Most of the current methods build upon the geostatistical model with a Gaussian process that relies on selecting ad hoc kernels to account for spatial expression patterns. To overcome this potential challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify spatially variable genes via Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the Ising model. Simulation results show that our energy-based modeling approach led to higher accuracy in detecting spatially variable genes than those kernel-based methods. Applying our method to two real spatial transcriptomics datasets, we discovered novel spatial patterns that shed light on the biological mechanisms. The proposed method presents a new perspective for analyzing spatial transcriptomics data.
rate research
Read More
Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistical challenges of traditional household surveying, official statistics can be slow to be updated; estimates that exist can be coarse, a consequence of prohibitive costs and poor infrastructures; and mass urbanisation can render manually surveyed figures rapidly out-of-date. Comparative judgement models, such as the Bradley--Terry model, offer a promising solution. Leveraging local knowledge, elicited via comparisons of different areas affluence, such models can both simplify logistics and circumvent biases inherent to house-hold surveys. Yet widespread adoption remains limited, due to the large amount of data existing approaches still require. We address this via development of a novel Bayesian Spatial Bradley--Terry model, which substantially decreases the amount of data comparisons required for effective inference. This model integrates a network representation of the city or country, along with assumptions of spatial smoothness that allow deprivation in one area to be informed by neighbouring areas. We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania.
The technology to generate Spatially Resolved Transcriptomics (SRT) data is rapidly being improved and applied to investigate a variety of biological tissues. The ability to interrogate how spatially localised gene expression can lend new insight to different tissue development is critical, but the appropriate tools to analyse this data are still emerging. This chapter reviews available packages and pipelines for the analysis of different SRT datasets with a focus on identifying spatially variable genes (SVGs) alongside other aims, while discussing the importance of and challenges in establishing a standardised ground truth in the biological data for benchmarking.
Alzheimers disease is the most common cause of dementia. It is the fifth-leading cause of death among elderly people. With high genetic heritability (79%), finding disease causal genes is a crucial step in find treatment for AD. Following the International Genomics of Alzheimers Project (IGAP), many disease-associated genes have been identified; however, we dont have enough knowledge about how those disease-associated genes affect gene expression and disease-related pathways. We integrated GWAS summary data from IGAP and five different expression level data by using TWAS method and identified 15 disease causal genes under strict multiple testing (alpha<0.05), 4 genes are newly identified; identified additional 29 potential disease causal genes under false discovery rate(alpha < 0.05), 21 of them are newly identified. Many genes we identified are also associated with some autoimmune disorder.
Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call joint modeling of multiple RNA-seq samples for accurate isoform quantification (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQs advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line.
An evolutionary tree is a cascade of bifurcations starting from a single common root, generating a growing set of daughter species as time goes by. Species here is a general denomination for biological species, spoken languages or any other entity evolving through heredity. From the N currently alive species within a clade, distances are measured through pairwise comparisons made by geneticists, linguists, etc. The larger is such a distance for a pair of species, the older is their last common ancestor. The aim is to reconstruct the past unknown bifurcations, i.e. the whole clade, from the knowledge of the N(N-1)/2 quoted distances taken for granted. A mechanical method is presented, and its applicability discussed.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
