buenrostrolab/stimatac_analyses_code

All code associated with manuscript detailing scATAC and scRNA-seq following stimulus of PBMCs

Functional Inference of Gene Regulation (FigR)

Core code associated with the approach outlined in our recent preprint detailing gene regulatory network inference using scATAC and scRNA-seq integration

FigR is a computational framework for supporting the integration of single cell chromatin accessibility and gene expression data to infer transcriptional regulators of target genes

FigR uses independently or concomitantly-profiled single-cell ATAC-seq (scATAC-seq) and scRNA-seq, to i) computationally-pair scATAC-seq and scRNA-seq datasets (if needed), ii) infers cis-regulatory interactions, and iii) defines a TF-gene gene regulatory network (GRN)

1. OptMatch pairing of scATAC and scRNA-seq cells

Optimal ATAC-RNA cell matching to accurately pair imbalanced, independently generated scATAC and scRNA-seq datasets for downstream integration and analyses

2. Determining domains of regulatory chromatin (DORCs) using paired scATAC-seq scRNA-seq information

Cis-regulatory correlation analysis framework to identify gene-peak (chromatin accessibility peak) significant associations and deduce key genes that are domains of such regulatory activity (DORCs)

3. Using FigR to determine transcriptional activators and repressors of target genes

Core component for functional inference of gene regulation using DORCs and TF motifs, to identify putative TF activators and regulators of gene activity

stimatac_analyses_code's Issues

BuenRTools package missing

Hi,

Very cool paper and thank you for making the code available. I've run into a problem where runFigR requires the package BuenRTools, which I cannot find anywhere. I think that the code from that was moved to utils.R, in which case line 142 of FigR_functions has to be modified so it doesn't require it.

Making a DORC SE

Hi!

Thank you so much for generating such powerful code! I am looking to run FigR but I am getting stuck at the dorcMat part.

DORC.SE <- readRDS("./data/SE/ATAC_stim_DORC_prom_enhancer_scores.rds")

I am wondering how you constructed the DORC prom enhancer RDS? I assume you use the super enhancer data from Hnisz, 2013... just not sure how

When is subsetting by condition advised?

Hi,

First of all, very cool tool. I was wondering at what stage of the analysis is it worth it to subset by conditions? E. g. I have condition "WT" and "MUT" and I want to discover the DORCs and TFs that are driving their difference (and are common between the two). In that case I would imagine running the RNA-ATAC pairing would be worth running within condition and then constructing the DORCs using the unified WT and MUT cell pairs (not separately on only MUT and only WT?). Afterwards should figR be run on the unified WT and MUT cells or separately?

Effect of relative number of RNA and ATAC cells

Hi, great job developing this tool for integrative analysis!
I noticed in the paper that most datasets had far more ATAC cells than RNA cells. Since cells from the modality with fewer cells are resampled during optimal matching, do you know how the relative number of RNA and ATAC cells affects matching and DORC inference? Is there any data on how DORC inference changes (if it does) when relative number of RNA and ATAC cells in the dataset are changed, especially in cases where RNA cells >> ATAC cells.

Thanks

Distance when define DORCs

Hi,

Thanks for your great work. I wonder why you choose the widow as 50kb but not 100kb or 500kb. Does the window size influence the results?

Thanks!

inquiry about Loading scATAC-seq matrices into R

Hi

i want to analyze some scATAC-seq data. And after unzip, i got 10 folders (1 patient per folder). In folder, there are 2 subfolders in whcih scRNAseq data and scATAC-seq data exist seperately. Within these 2 subfolders, there are files generated by cell ranger ( i attached 2 pic.)
How may i loading scATAC-seq matrices as well as scRNAseq data into R? Could you kindly provide some codes?