ESICCC ESICCC: A systematic computational framework for evaluation, selection and integration of cell-cell communication inference methods
Manual
Workflow
Step0_LRToolsFunction contains the R/Python/Shell scripts that package the running code of 19 methods with Seurat objects as input into function.
- Step1_LRPredictionResult contains the R/Shell scripts to run 19 methods for inferring LR pairs from the 14 scRNA-seq datasets.
- Step2_PreSTForLRBench contains the R scripts to get the different ratios (e.g.top 10%, 20%, 30%, 40%) of cell type specific close and distant cell pairs in each dataset for the preparation of the benchmarking using mutual infomation.
- Step3_MIForLRBench contains the R scripts to calculate MI of LR interactions predicted by methods in the different ratios of cell type specific close and distant groups and calculate DLRC index of methods in each dataset.
- Step4_SIRSIForLRBench contains the R scripts to benchmark the similarity (SI and RSI) of the LR interactions predicted by each two methods.
- Step5_BenchBasedCAGEProteomic contains the R scripts to benchmark the 18 LR inference methods using the CAGE expression and proteomics data.
- Step6_LRBenchSampling contains the R/Shell scripts to run the 18 LR inference methods for inferring LR pairs from 70 sampled scRNA-seq datasets.
- Step7_LRBenchSamplingBench contains the R/Shell scripts to calculate Jaccard index between the LR pairs predicted based on the sampled datasets and the original datasets, and record the running time and maximum memory usage of methods in each dataset.
- Step8_LRTToolsFunction contains the R/Python/Shell scripts to run the 5 LR-Target inference methods for predicting ligand/receptor-targets using ST datasets as input.
- Step9_LRTBench contains the R scripts to benchmark the 5 LR-Target inference methods using cell line perturbation datasets for evaluation, and record the running time and maximum memory usage of methods in each dataset.
Datasets
- scRNA-seq and ST datasets
| Tissue (Disease) | SampleID (scRNA-seq) |
SampleID (ST) |
Literature PMID | Download URL (scRNA-seq) |
Download URL (ST) |
Evaluation purpose |
|---|---|---|---|---|---|---|
| Heart Tissue (Health) | CK357 | control_P7 | 35948637 | URL | URL | LR interactions LR-Target regulations |
| CK358 | control_P8 | |||||
| Heart Tissue (ICM) | CK368 | FZ_GT_P19 | LR interactions | |||
| CK162 | FZ_GT_P4 | |||||
| CK362 | RZ_P11 | |||||
| Heart Tissue (AMI) | CK361 | IZ_P10 | ||||
| CK161 | IZ_P3 | |||||
| CK165 | IZ_BZ_P2 | |||||
| Tumor Tissue (Breast cancer) |
CID44971 | CID44971 | 34493872 | URL | URL | LR interactions LR-Target regulations |
| CID4465 | CID4465 | |||||
| Mouse embryo | —— | Slide14 | 34210887 | —— | URL | LR interactions |
| PBMC | PBMC4K | —— | —— | URL | —— | LR interactions |
| PBMC6K | —— | —— | URL | —— | ||
| PBMC8K | —— | —— | URL | —— | ||
| Tumor Tissue (Gliomas) |
—— | UKF243_T_ST | 35700707 | —— | URL | LR-Target interactions |
| —— | UKF260_T_ST | |||||
| —— | UKF266_T_ST | |||||
| —— | UKF334_T_ST |
- Cell line perturbation datasets
| Datasets | Ligand/Receptor | Type | Condition | Cell Line | Disease |
|---|---|---|---|---|---|
| GSE120268 | AXL | receptor | Knockdown | MDA-MB-231 | Breast Cancer |
| GSE157680 | NRP1 | receptor | Knockdown | MDA-MB-231 | |
| GSE15893 | CXCR4 | receptor | Mutant | MDA-MB-231 | |
| CXCL12 | ligand | Treatment | MDA-MB-231 | ||
| GSE160990 | TGFB1 | ligand | Treatment | MDA-MB-231 | |
| GSE36051 | DLL4(1) | ligand | Treatment | MCF7 | |
| DLL4(2) | ligand | Treatment | MDA-MB-231 | ||
| JAG1 | ligand | Treatment | MDA-MB-231 | ||
| GSE65398 | IGF1(1) | ligand | Treatment | MCF7 | |
| GSE7561 | IGF1(2) | ligand | Treatment | MCF7 | |
| GSE69104 | CSF1R | receptor | Inhibit | TAMs | Gliomas |
| GSE116414 | FGFR1 | receptor | Inhibit | GSLC | |
| GSE206947 | EFNB2 | ligand | Treatment | cardiac fibroblasts | Health |
| GSE181575 | TGFB1 | ligand | Treatment | cardiac fibroblasts | |
| GSE123018 | TGFB1 | ligand | Treatment |
cardiac fibroblasts |
Tools for inferring intercellular LR pairs
- CellPhoneDB (Python, version: 3.0.0)
- CellTalker (R, version: 0.0.4.9000)
- Connectome (R, version: 1.0.1)
- NATMI (Python)
- ICELLNET (R, version: 1.0.1)
- scConnect (Python, version: 1.0.3)
- CellChat (R, version: 1.4.0)
- SingleCellSignalR (R, version: 1.2.0)
- CytoTalk (R, version: 0.99.9)
- CellCall (R, version: 0.0.0.9000)
- scSeqComm (R, version: 1.0.0)
- NicheNet (R, version: 1.1.0)
- Domino (R, version: 0.1.1)
- scMLnet (R, version: 0.2.0)
- PyMINEr (Python, version: 0.10.0)
- iTALK (R, version: 0.1.0)
- cell2cell (Python, version: 0.5.10)
- RNAMagnet (R, version: 0.1.0)
Tools for predicting ligand/receptor-targets regulations
- CytoTalk (R, version: 0.99.9)
- NicheNet (R, version: 1.1.0)
- stMLnet (R, version: 0.1.0)
- MISTy (R, version: 1.3.8)
- HoloNet (Python, version: 0.0.5)