This archive contains benchmarking input data and results for using single cell gene expression data to infer gene regulatory networks (GRN) by the Causal Inference with Composition of Transactions (CICT) method and a selected set of published methods. This accompanies the manuscript "Robust discovery of gene regulatory networks from single-cell gene expression data using Causal Inference with Composition of Transactions" (Shojaee and Huang, 2023). The original CICT algorithm was described in Shojaee et al. (arXiv:1608.02658, 2016). The benchmarked methods were included in the BEELINE benchmarking pipeline (Pratapa et al., Nat Methods 2020), to which we added DEEPDRIM (Chen et al., Brief Bioinform 2021) and Inferelator 3.0 (Gibbs et al., Bioinformatics 2022). The output directory names are: CICT_v2/: CICT DEEPDRIM72_v2/: DEEPDRIM INFERELATOR38_v2/: Inferelator-Prior INFERELATOR34_v2/: Inferelator-NoPrior GENIE3/: GENIE3 GRNBOOST2/: GRNBOST2 LEAP/: LEAP PIDC/: PIDC PPCOR/: PPCOR SCNS/: SCNS SCODE/: SCODE SCRIBE/: SCRIBE SINCERITIES/: SINCERITIES SINGE/: SINGE RANDOM/: RANDOM The methods were benchmarked against two kinds of scRNA-seq datasets. Simulated datasets produced by the SERGIO simulator from a synthetic network (Dibaeinia et al., Cell Systems 2020), including complete datasets and datasets with dropouts. Experimental datasets compiled by the BEELINE pipeline, evaluated at three different levels L0, L1 and L2, and three types of ground truth networks: Evaluation levels: L0: 500 highly varying genes plus TFs L1: 1000 highly varying genes plus TFs L2: 500 highly varying genes, TFs and 500 genes randomly selected that excluded the 1000 highly varying genes from L1. Types of ground truths: Cell-type-specific ChIP-seq ground truth (L0, L1, L2) Non-specific ChIP-seq ground truth (L0_ns, L1_ns, L2_ns) Loss-of-function/gain-of-function ground truth (L0_lofgof, L1_lofgof, L2_lofgof) The directory structure is organized in accordance with the BEELINE benchmarking pipeline. For details please please see the BEELINE documentation (https://murali-group.github.io/Beeline/) and Github repo (https://github.com/Murali-group/Beeline).