Dimensionality reduction and data integration for scRNA-seq data based on integrative hierarchical Poisson factorisation
Wong, T.; Barahona, M.
Show abstract
Single-cell RNA sequencing (scRNA-seq) data sets consist of high-dimensional, sparse and noisy feature vectors, and pose a challenge for classic methods for dimensionality reduction. Such problems are compounded when dealing with composite data sets formed by different batches. We introduce Integrative Hierarchical Poisson Factorisation (IHPF), an extension of HPF that makes use of a noise ratio hyper-parameter to tune the variability attributed to batches vs. biological sources (cell phenotypes). We exemplify the application of IHPF under different data integration scenarios with varying alignments of batches and cell diversity, and show that IHPF produces latent factors that can be advantageously applied for cell clustering and visualisation. In addition, the extracted factors have a dual block structure in both cell and gene spaces with enhanced biological interpretability.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error 97%
- Mcadet: a feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection 97%
- Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.