Perturbational modeling in single-cell -omics computationally captures, at unprecedented cellular resolution, responses to molecular changes initiated by gene knockdowns or drug treatments. Notwithstanding, relevant in silico tools are hindered by interoperability issues, hefty computational demands, and reliance on complex algorithms like Deep Learning that lack biological interpretation. Here, we introduce "PertFlow", a user-friendly, cloud-based workflow merging standard single-cell pipelines for scRNA-seq/ECCITE-seq with specialized perturbational modeling tools. PertFlow offers seamless Seurat and Scanpy interoperability through in-tandem Python and R coding (Rpy2 package). A Google Colab implementation of the method demonstrates the ease of deployment and allows for testing by other users. At first, PertFlow enables pathway/transcription factor (TFs) enrichment (DecoupleR) to establish the necessary biological context. At its core, PertFlow employs AugurPy for cell-type prioritization, scGEN variational autoencoder for perturbation response prediction, and MixScape for assessing perturbations in single-cell pooled CRISPR screens (ECCITE-seq). Moreover, PertFlow also features the CPA compositional autoencoder for complex perturbational predictions and the ASGARD toolkit for drug repurposing based on LINCS L1000 project data. When applied to Chronic Lymphocytic Leukemia (CLL) scRNA-seq data from peripheral blood cells, pre/post-Ibrutinib therapy (PMID: 31996669), PertFlow was able to capture biological ground truths (suppression of oxidative phosphorylation)(DecoupleR), but also went beyond them, showing: (a) cell prioritization of monocytes 30 days post-Ibrutinib and implication of galectins in a poor CLL Ibrutinib responder (AugurPy), (b) perturbational predictions for CLL-geared TFs like IRF1 (MixScape) (c) repurposed drugs mimicking Ibrutinib’s effects like auranofin, fostamatinib, parthenolide, vorinostat, idelalisib and sonidegib (ASGARD).

https://github.com/BiodataAnalysisGroup/PertFlow_A-cloud-based-workflow-for-scRNA-seq-perturbational-modeling