Measures progression through branching lineages using a random-walk-based distance in diffusion map space. DPT allows for branching and pseudotime analysis on large-scale RNA-seq data sets. This package is significantly more robust with respect to noise in low-density regions and cell outliers than existing methods, which rely on the estimation of minimum spanning trees or sampling-based distances. Furthermore, DPT is able to remove asynchronity of scRNA-seq snapshot data from several days, aligning cells in terms of their degree of differentiation.
A flexible statistical framework for the analysis of single-cell RNA sequencing data. MAST is suitable for supervised analyses about differential expression of genes and gene modules, as well as unsupervised analyses of model residuals, to generate hypotheses regarding co-expression of genes. MAST accounts for the bimodality of single-cell data by jointly modeling rates of expression (discrete) and positive mean expression (continuous) values. Information from the discrete and continuous parts is combined to infer changes in expression levels using gene or gene set-based statistics. Because our approach uses a generalized linear framework, it can be used to jointly estimate nuisance variation from biological and technical sources, as well as biological effects of interest.
Predicts drug sensitivity of single cells within human tumors. SCATTome is an R software package that uses machine learning approaches to build binary classification models, selects top significant genes, predicts drug response of individual cells and computes each cell’s probability of response based on the targeted transcriptome signature. This package can be used in other cancer models to predict heterogeneity of drug response in individual cells based on targeted single-cell gene expression analysis and may also be used to identify minimal residual disease (MRD).
A framework for resolving differences in gene expression patterns for the early mouse embryo based on single-cell gene expression data. Therefore, a nonlinear mapping between a low-dimensional latent space and the high-dimensional data space was combined with gene relevance maps and gradient plots in order to ensure interpretability.
An approach to the inference of gene regulatory network (GRN) from single-cell expression data. It integrates the structure of a cell lineage tree with transcriptional patterns from single-cell data. This method adopts probabilistic Boolean network (PBN) for network modeling, and genetic algorithm as search strategy. Guided by the 'directionality' of cell development along branches of the cell lineage tree, this approach is able to accurately infer the regulatory circuits from single-cell gene expression data, in a holistic way.
A webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.
A useful method for the identification of subpopulations in low-dimensional expression data, as emerging from qPCR or FACS measurements. With MCA it is possible to investigate the robustness of covariate correlations with respect subpopulations, graphically identify outliers, and identify factors contributing to differential regulation between pairs of covariates. MCA thus provides a framework for investigation of expression correlations for genes of interests and biological hypothesis generation.
An approach for performing probabilistic principal component analysis (PCA) for censored data within the framework of Gaussian process latent variable models (GPLVMs). We showed that for single-cell qPCR data with a high fraction of censored data points, the resulting probabilistic (kernel) PCA representations reflected the true structure of the data better than conventional approaches.