integrated transcription factor analysis for single cell data (iTFSC) is an R package designed to consolidate transcription factor (TF) information across multiple tools to arrive at a more robust list of TF. The package also allows users to do downstream visualization including differential expression analysis. The package will be validated using four different cancer types.
In order to run this package you will need to install the following dependencies: (please use R version 4.1.1)
- library(Seurat)
- library(SeuratDisk)
- library(SCENIC)
- library(BITFAM)
- library(Dorothea)
- library(piano)
- library(ggplot2)
- library(dplyr)
- library(tidyr)
- library(AUCell)
- library(RcisTarget)
- library(GENIE3)
- library(base)
- library(tibble)
- library(ComplexHeatmap)
- library(ggVennDiagram)
- library(reshape2)
- library(piano)
- library(ggpubr)
If there is any issues install SCENIC, please visit this link for installation: http://htmlpreview.github.io/?https://github.com/aertslab/SCENIC/blob/master/inst/doc/SCENIC_Setup.html
Description: develop an integrated transcription factor analysis tool for single-cell and bulk data. The tool will include 4-5 existing transcription factors tools (eg SCENIC, DORTHEA, BITFAM etc) for single-cell data combined to give the users the transcription factor probability generated by a combined analysis. One way to select the best transcription factor is simply extracting the most common transcription factor generated from multiple tools. Other ways are to use the differential expression to decide on the best transcription factor across different cell types and find a common or high probability one. I am doing something similar for my research project, but I always thought it would be helpful if there was a package or tool to do this for me. The main idea would be to ensure that the transcription factors that we are getting are the ones that are actually involved, and this would be done by reproducibility across tools and through other downstream analyses.
- integrated and fast TF analysis using 4-5 existing tools
- extract common TFs generated from all tools
- differential expression between cell types using limma on the output of the results from different tools
- GSEA on the results from differential expression analysis
- (if time) Apply the tools for bulk data (given there are at least 100 patients)
- (if time) use the transcription factors for the deconvolution of bulk data
The RDS file for these datasets can be downloaded from here: https://drive.google.com/drive/folders/1WL0TxDAQpPGzmGy8gltT-x-ezSw6Ndh1?ths=true
How the user will run the example data:
- Download the data from ### Example data (link is above) The RDS file for these datasets can be downloaded from here: https://drive.google.com/drive/folders/1WL0TxDAQpPGzmGy8gltT-x-ezSw6Ndh1?ths=true
- user can directly use the example data in the functions, no further processing is required is the data is already processed by standard seurat pipeline, please refer to this tutorial for converting your data into a standard seurat object: https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
Expected results:
- a robust list of TF generated using three methods for extracting transcriptional activity score from single cell data
- we will also provide a venn diagram to show how many common TFs exists
- we will also employ heatmaps as depicted in the workflow image below to show individual methods output