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Current tools Flashcards

1
Q

Who are the authors of CellPhoneDB?

A

Mirgana Efremova and Roser Vento-Tormo (First and contact author)

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2
Q

CellPhoneDB

A

Uses co-expression of ligand-receptor pairs to infer active cell-cell communication.
Uniquely accounts for multi-subunit protein complexes in ligand-receptor interactions.

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3
Q

CellChat

A

Employs a probability-based approach using expression of ligands, receptors, and cofactors.
Uniquely incorporates pathway analysis and considers signaling roles beyond just ligand-receptor pairs.

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4
Q

What model does CellChat incorporate?

A

Applies a mass action-based model to quantify communication probability

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5
Q

Who are the authors of CellChat?

A

Suoqin Jin (First author)
Qing Nie (Senior author)

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6
Q

NicheNet

A

Uses a network-based approach combining ligand-receptor interactions with intracellular signaling and transcriptional regulation.
Uniquely predicts target genes affected by intercellular signaling.

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7
Q

Who are the authors of NicheNet?

A
  1. Robin Browaeys (First author)
  2. Yvan Saeys (Senior author)
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8
Q

How did NicheNet validate itself?

A
  1. Compared to randomized networks and models with fewer data sources.
  2. Evaluated using several classification metrics such as AUC-iRegulon, AUPR, and AUROC
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9
Q

CellTalker

A
  1. Uses a threshold-based method, evaluating ligand-receptor expression levels exceeding predefined thresholds.
  2. Uniquely scores interactions by jointly weighting ligand and receptor expression levels
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10
Q

Giotto

A

Integrates spatial information to identify cellular neighborhoods and interactions. Uniquely provides tools to explore the effect of neighboring cell types on gene expression

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11
Q

SingleCellSignalR

A

Employs a novel regularized score to infer ligand-receptor interactions. Uniquely attempts to assess confidence in predicted interactions and control false positives

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12
Q

iTALK

A

Characterizes intercellular communication signals in multicellular environments.
Uniquely provides functional annotation of ligand-receptor genes using a curated database and offers various visualization options

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13
Q

SpaOTsc

A

Uses optimal transport to map single-cell RNA-seq data to spatial transcriptomics data, inferring spatial constraints on cell-cell communication.
Uniquely incorporates both physical distance and gene expression to estimate signaling pathway spatial distances.

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14
Q

CCCExplorer

A

Integrates a computational model to uncover cell-cell communication as a direct and connected network.
Uniquely predicts and visualizes gene signaling networks, ranging from ligand-receptor interactions to transcription factors and their target genes, specifically aiding research on crosstalk identification in the tumor microenvironment

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14
Q

stLearn

A
  1. Integrates spatial location, tissue morphology, and gene expression to infer cell-cell communication.
  2. Uniquely uses pseudo-space-time distance combining physical and gene expression distances to reconstruct spatial transition gradients within and between cell types.
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15
Q

SoptSC

A

Uses optimization to infer cell lineages and communication networks from single-cell transcriptomics data.
Uniquely considers both intercellular and intracellular gene-gene interactions in receiver cells.

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16
Q

NATMI

A

Predicts cell-cell communication networks using a curated database of ligand-receptor pairs.
Uniquely allows analysis of autocrine signaling and visualization of highly communicating cellular communities.

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17
Q

ICELLNET

A

Uses a curated database of ligand-receptor interactions and quantifies communication scores between cell types. Uniquely accounts for multiple subunit complexes and allows connecting a cell population of interest with reference cell types.

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18
Q

LIANA

A

Integrates multiple existing methods and resources to provide a consensus approach.
Uniquely allows flexible combination of different inference methods and prior knowledge databases.

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19
Q

scMLnet

A

Constructs multi-layer signaling networks by integrating intercellular (ligand-receptor) and intracellular (receptor-TF-target gene) pathways.
Uniquely incorporates intracellular signaling to predict activated transcription factors and target genes.

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20
Q

Who are the author of scMLnet

A

Jinyu Cheng (first author)
Xiaoqiang Sun (Senior author)

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21
Q

PyMINEr

A

Automates cell type identification, pathway analysis, and detection of autocrine-paracrine signaling networks from scRNA-seq data.
Uniquely performs graph theory-based analysis of gene regulation.

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22
Q

What are the key assumptions of PyMINEr?

A

Assumes that graph structures can clarify potential etiologies of disease-associated variants

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23
Q

Connectome

A
  1. Treats cell types as nodes and ligand-receptor interactions as edges in a network
  2. Applies a system-wide Wilcoxon rank sum test to assign p-values to edges;
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24
Q

scTensor

A

Uses tensor decomposition to detect many-to-many cell-cell interactions. Uniquely extracts representative triadic relationships (hypergraphs) of ligand-receptor-target gene interactions.

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24
Q

CytoTalk

A

Constructs cell-cell communication networks using prize-collecting Steiner Forest algorithms. Uniquely integrates ligand-receptor interactions with intracellular signaling pathways.

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25
Q

What are the main strengths of CytoTalk?

A
  1. Enables de novo construction of signal transduction pathways;
  2. Integrates both intracellular and intercellular interactions;
  3. Considers network topology in identifying signaling pathways;
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26
Q

Who are the authors of CytoTalk?

A

Yuxuan Hu (First author)
Kai Tan (Senior author)

27
Q

Tensor-cell2cell

A

Uses tensor decomposition to analyze cell-cell communication patterns across multiple samples or conditions.
Uniquely enables unsupervised discovery of communication programs across contexts.

28
Q

SpaTalk

A

Integrates spatial information with gene expression to infer cell-cell interactions in spatial transcriptomics data.
1. Uses a graph network and knowledge graph to model and score the ligand-receptor-target (LRT) signaling network.
2. Dissects cell-type composition through a non-negative linear model (NNLM) and spatial mapping between single-cell RNA-seq and spatial transcriptomic data.
3. Constructs a cell graph network using the K-nearest neighbors (KNN) algorithm to identify spatially proximal cells.
4. Applies permutation tests to filter and score significantly enriched ligand-receptor interactions (LRIs).

29
Q

scConnect

A

Infers cell-cell interactions using a statistical approach that accounts for gene co-expression. Uniquely provides a measure of interaction specificity.
1. Estimates expression of molecular ligands synthesized by multiple enzymes
2. Uses interaction information from Guide to Pharmacology to identify putative cell-cell interactions
3. Applies random permutation of cell type labels to estimate specificity of ligands and receptors for each cell type
4. Stores interactions in a multi-directional graph structure

29
Q

COMMOT

A
  1. Uses optimal transport to infer cell-cell communication from single-cell data. Uniquely considers the global structure of the data to identify significant interactions.
  2. Incorporates spatial distances between cells as constraints
29
Q

Domino

A
  1. Reconstructs intercellular signaling based on transcription factor activation
  2. Links transcription factor activation with expression of receptors and their possible ligands
  3. Constructs signaling networks with SCENIC independent of cell clustering
30
Q

What are the key assumptions of Domino?

A
  1. Transcription factor activation indicates active signaling pathways
  2. Changes in gene expression in receiver cells result from ligand-receptor interactions
  3. Signaling networks can be constructed independently of cell type clustering
31
Q

scSeqComm

A
  1. Identifies and quantifies evidence of ongoing intercellular and intracellular signaling from scRNA-seq data
  2. Incorporates transcription factor activation for intracellular signaling
  3. Constructs signaling networks based on gene expression data
  4. Provides functional characterization of inferred cellular communication
32
Q

Scriabin

A

Performs cell-cell communication analysis at single-cell resolution without aggregation. Uniquely incorporates models of downstream intracellular signaling and gene network analysis.

33
Q

What are the main strengths of Scriabin?

A
  1. Can uncover spatial features of interaction from dissociated data
  2. Identifies communication networks that may be obscured by agglomerative methods
34
Q

SpaCi

A

Infers cell-cell interactions in spatial transcriptomics data using a spatial clustering approach. Uniquely considers both local and global spatial patterns.

35
Q

What are the key assumptions of spaCI?

A
  1. Spatial proximity influences cell-cell communication
  2. Dropout events and noisy signals in spatial transcriptomics data can be addressed through adaptive modeling
36
Q

SpatialDM

A

Uses deep learning to infer cell-cell interactions from spatial transcriptomics data. Uniquely captures complex spatial dependencies.

37
Q

What are the main strengths of SpatialDM?

A
  1. Prioritizes interaction features rather than just cell-type pairs
  2. Identifies interaction spots at single-spot resolution in spatial context
  3. Scalable to millions of spots due to analytical null distribution
38
Q

scLR

A

Employs a statistical approach to infer ligand-receptor interactions from scRNA-seq data. Uniquely provides a measure of interaction strength and specificity.

39
Q

What are the main strengths of scLR?

A
  1. Designed for single-cell RNA-seq datasets with small sample sizes
  2. Provides high sensitivity and specificity in detecting dysregulated ligand-receptor interactions
40
Q

LRLoop

A

Uses a network-based approach to infer ligand-receptor interactions and feedback loops. Uniquely identifies potential autocrine and paracrine signaling circuits.

41
Q

What are the key assumptions of LRLoop?

A
  1. Feedback loops between two cell types are a widespread and vital signaling motif
  2. Cell-type-specific signaling and regulatory networks influence communication strength
42
Q

What are the strength of LRLoop?

A
  1. Assessed using bulk datasets, demonstrating reduced false positive rates
  2. Used between-tissue interactions as an indicator of potential false-positive predictions
43
Q

scTenifoldXct

A

Employs tensor decomposition to infer cell-cell interactions across multiple conditions. Uniquely allows comparison of interaction patterns across experimental conditions.

44
Q

What are the key assumptions of scTenifoldXct?

A

Manifold alignment can capture relevant cell-cell interaction information

45
Q

What are the main strengths of scTenifoldXct?

A
  1. Detects weak but biologically relevant interactions overlooked by other methods
  2. Can compare different samples (e.g., healthy vs. diseased) to identify differential interactions
46
Q

DiSiR

A

Uses a permutation-based framework to identify ligand-receptor interactions at the subunit level from single-cell RNA-sequencing (scRNA-seq) data.

47
Q

SPRUCE

A

Uses a statistical approach to infer cell-cell interactions from spatial transcriptomics data. Uniquely accounts for spatial heterogeneity and technical artifacts.

48
Q

Renoir

A

Employs a network-based approach to infer cell-cell interactions and regulatory networks. Uniquely integrates multiple omics data types.

49
Q

HiVAE

A

Employs a variational autoencoder approach to infer cell-cell interactions. Uniquely handles high-dimensional and sparse single-cell data.

50
Q

What are the key assumptions of HiVAE?

A

Hierarchical structure can effectively model complex cellular interactions

51
Q

Calligraphy

A
  1. Uses a graph neural network (GNN) to model cell-cell interactions
  2. Constructs a cell-cell interaction graph based on spatial proximity or other relevant metrics
  3. Learns cell type-specific communication patterns
52
Q

CellCallEXT

A
  1. Extends CellCall to analyze inter- and intracellular communication pathways
  2. Incorporates Reactome pathway information
  3. Quantitatively integrates expression of ligands, receptors, TFs, and target genes
53
Q

What are the main strengths of CellCallEXT?

A
  1. Can directly handle two-condition comparisons (e.g., tumor vs. healthy)
  2. Identifies altered L-R pairs and downstream gene regulatory networks
  3. Provides pathway enrichment analysis for interpreting disease-specific changes
  4. Extends the L-R-TF datasets with Reactome repertories
54
Q

Who are the authors of CellCallEXT?

A
  1. Shougou Gao (First author)
  2. Neal S Young (Senior author)
54
Q

GraphComm

A

Uses a graph-based deep learning approach to predict cell-cell communication. Uniquely incorporates both cellular signaling networks and transcriptomic profiles to provide comprehensive information on intercellular communication.

55
Q

** NEST**

A

Extracts spatial structure through coexpression hotspots - regions exhibiting localized spatial coexpression of gene sets.
Uniquely applies a 3D diffusion model to compute cell-cell interactions across different tissue layers when 3D spatial data is available.

56
Q

SpaCCC

A

Employs a large language model-based approach for inferring cell-cell communications from spatially resolved transcriptomic data.
Uniquely combines an LLM with functional gene interaction networks to embed ligand and receptor genes into a unified latent space.

57
Q

** TraSig**

A

Utilizes pseudotime ordering information from single-cell RNA-seq data to infer cell-cell interactions. Uniquely identifies significant ligand-receptor pairs with similar trajectories to score interacting cell clusters.

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CCC (1 decks)

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