| Dynamic network (extensively reviewed in refs [53–55])
|
SCNS [81] |
Single-gene changes between cell transition states can inform on gene regulatory relations |
Boolean |
Does not rely on prior knowledge. Has a web UI. Resulting models are executable and can be used to make predictions |
Need data discretization; limit to small numbers of genes; regulatory relations need to follow Boolean rules |
| SCODE [82] |
TF expression dynamics (pseudo-time) and TF regulatory relations (GENEI3) |
ODE; Bayesian model selection |
Estimate relational expression efficiently using linear regression; reduction of time complexity; fast algorithm |
Need dimension reduction first for computing speed and memory feasibility; assumes that all cells are on the same trajectory; optimization is computationally intractable |
| GRISLI [83] |
Variability in scRNAseq data caused by cell cycle, states, etc. allows the inference of pseudo-time associated with each individual cell |
ODE |
Makes no restrictive assumption on the gene network structure; can consider multiple trajectories; fast algorithm |
Has to estimate the velocity of each individual cell using information from neighbors |
| SINCERITIES [84] |
Changes in the expression of a TF will alter the expression of target genes |
Ridge regression and partial correlation analysis |
Low computational complexity and able to handle large-scale data |
Requires scRNAseq data at multiple time points. Restricted to TFs and their targets to infer edges |
| Scribe [85] |
Cell ordering can be improved with time-series or cell velocity estimations |
RDI |
Outperforms other pseudo-time methods given time-series data. Can be applied to any data type if the data structure is appropriate |
Requires time-ordered gene expression profiles or velocity estimation from introns and exons |
| AR1MA1-VBEM [40] |
The cell differentiation process or response to external stimulus reveals the hierarchical structure of the transcriptome |
First-order autoregressive moving-average and variational Bayesian expectation-maximization |
Weighted interactions between genes along psuedotime. Model used accounts for noisy data |
Data are expressed as fold changes between timepoints/conditions or scaled by housekeeping genes |
| SCINGE [86] |
Learned target regulator genes can be used to assign each cell to their progress along a trajectory |
Granger causality |
Smooths irregular pseudo-times and missing expression values |
Near random performance for predicting targets of individual regulators |
| SoptSC [87] |
Similarities between whole transcriptomes of single cells can be used to order them |
Cells ordered by minimum paths on weighted cluster-to-cluster graph derived from cell similarity matrix |
Includes comprehensive single-cell workflow; leverages information from other parts of the workflow to improve performance |
Cannot be run with other tools, have run the full workflow to get pseudo-time inference |
| Within-cell or cell population network |
SCENIC [88] |
TF target-based regulation |
Combining TF regulatory relations (GENIE3) with TF-binding motif analysis |
Robust against dropouts, get a TF score for individual cells (no averaging of cells). |
Limited to TF-based relations |
| Pina et al. [89] |
TFs drive lineage commitment |
Odds ratio for on/off gene associations and spearmen correlation for expression levels associations |
Robust to dropouts |
Based on single-cell multiplex qRT-PCR, may be difficult to extend the method to sparse single-cell data (selected 44 genes to test) |
| Iacono et al. [90] |
Coexpression is regulated by TFs, cofactors, and signaling molecules which can be captured with gene–gene correlations |
Pearson correlation using z-score-transformed counts |
Can compute correlations at the single-cell level and it is robust to dropouts and noise inherent to single-cell data |
Networks are very dense (some have millions of significant edges) |
| PIDC [39,91] |
Gene regulatory information reflected in dependencies in the expression patterns of genes |
Partial information decomposition using gene trios |
Compared with correlation, captures more complicated gene dependencies |
Networks are influenced by data discretization, choice of mutual information estimator, method developed for sc-qPCR data, may not be extendable to higher throughput and sparser scRNAseq data |
| Jackson et al. [92] |
Deletion of TFs combined with experimental conditions allows for the inference of gene relationships |
MTL to leverage cross-dataset commonalities and incorporate prior knowledge |
Does not require sophisticated normalization of single-cell data or imputation. Able to combine multiple conditions/datasets for more accurate inference. TF deletions give strong causal link to affected genes |
Requires single-cell data with TF deletions and/or environmental perturbations |
| Wang et al. [93] |
Gene perturbations allow for inference of causal relationships |
Scoring of conditional independence test to identify optimal DAG |
Gives causal relationships between genes |
Requires interventional data. No loops allowed in DAG |
| ACTION [94] |
Functional identity of cells is determined by a weak, but specifically expressed set of genes which are mediated by TFs |
Kernel-based cell similarity and geometric approach to identify primary functions |
Robust to dropout and does not require averaging. Identifies functions unique to cell types |
Requires TFs and their targets. Only provides TF-driven networks |
| SINCERA [95] |
TF target-based regulation |
First-order conditional dependence on gene expression to construct a DAG |
Key TFs identified using multiple importance metrics |
Only considers TFs and their targets. Requires genes/TFs to be DEGs or expressed in >80% of cells |
| Cell–cell communication network |
iTALK [96] |
Ligand–receptor interactions |
Threshold ranked list of genes from two cell types for ligand–receptor pairs |
Allows for the inference of directionality of interaction |
Requires curation of ligand–receptor interactions (not all interactions are known). Average expression at the cell-type level (no longer single cell). Cannot reveal novel interactions beyond known ligand–receptor knowledge |
| Zhou et al. [97] |
Ligand–receptor interactions |
Expression of ligand and corresponding receptor more than three standard deviations greater than the mean |
Allows for the inference of directionality of interaction |
Requires curation of ligand–receptor interactions (not all interactions are known). Average expression at the cell-type level (no longer single cell) |
| Kumar et al. [98] |
Ligand–receptor interactions |
Product of the average expression of ligand and corresponding receptor |
Allows for the inference of directionality of interaction. Interaction score gives the strength of interaction (rather than just significance) |
Requires curation of ligand–receptor interactions (not all interactions are known). Average expression at the cell-type level (no longer single cell) |
| Arneson et al. [99] |
Ligand to downstream signaling |
Coexpression of ligand genes in source cells with other genes in target cells |
Use secreted ligands as a guidance for directional inference between cell populations |
Gene expression is summarized to the cell population level and coexpression is at the sample level, requiring large sample sizes |
| SoptSC [87] |
Ligand–receptor interactions |
Likelihood estimate of the interaction between two cells based on expression of the ligand, receptor, and downstream pathway target genes (including expression direction). Consensus signaling network derived from all cells in each cluster |
Incorporates target genes of pathways and their directionality. Computes interaction likelihood at the single-cell level and summarizes across all cells in the cluster for higher confidence |
Requires curation of ligand–receptor interactions and their downstream pathways |
| scTensor [100] |
Ligand–receptor interactions |
Tensor decomposition with cell–cell interactions as hypergraphs |
Allows L–R pairs to function across multiple cell-type pairs (not restricted to a single-cell-type pair), which is more reflective of underlying biology |
Requires curation of ligand–receptor interactions. Averages single cells to the cell-type level |
