Author: Minghan Li

Environment: R

Correspondence: If you have any questions, please ask on GitHub or contact mhli23@m.fudan.edu.cn.

Before using ReCIDE, please make sure the following packages are installed in your R environment. Sometimes, different versions of R packages may not cause significant issues, but it is beneficial to keep R package versions consistent.

Load the required dependencies:

library(DWLS)
library(Seurat)
library(fastSave)
library(COSG)
library(stringr)
library(dplyr)
library(ggplot2)
library(ggbiplot)
library(pbmcapply)
library(mclust)
library(gmodels)

Download "ReCIDE_all_function.R" and load it:

source('/your_path/ReCIDE_all_function.R')

The DEMO data is from the cross datasets test of PFC, and all data can be obtained from https://drive.google.com/drive/folders/1TRz7Xj4Ddvt3NMTOHbpiTJBkTbDuaXzf?usp=drive_link. In addition to the reference single-cell RNA-Seq matrix, other data can also be obtained from ReCIDE/DEMO_data at main · limingham/ReCIDE (github.com) .

##Load single-cell reference and pseudo-bulk datasets
SC_ref = readRDS.lbzip2('/your_path/DEMO_data/REF_PFC.rdsFS',n.cores=50)
EXP_df = readRDS('/your_path/DEMO_data/EXP_PFC_df.rds')

##Obtaining reference cell type labels and subject labels
##Please note that the cell type labels should not contain any symbols other than "_".

celltype = SC_ref@meta.data[['cell type label']] ##In the demo data, it is major.celltype.
subject = SC_ref@meta.data[['subject labels']] ##In the demo data, it is subject.

(Optional) If computational resources are insufficient (<256GB), subjects in SC_ref can be sampled.

set.seed(123)
sample_subject=sample(unique(SC_ref@meta.data[["subject"]]),10)
SC_ref=subset(SC_ref,subject %in% sample_subject)

ref_MGM.list = MGM_build(SC_ref = SC_ref,
                        EXP_df = EXP_df,
                        label_celltype = celltype,
                        label_subject = subject,
                        n_cores = 50)

SC_ref: single-cell expression matrix (row: gene, col: cell) ，Seurat or data.frame object

EXP_df: bulk expression matrix (row: gene, col: subject) ，data.frame object

label_celltype: Celltype labels corresponding to each cell in SC_ref

label_subject: Subject labels corresponding to each cell in SC_ref

n_cores: Number of parallel cores, default 5

ReCIDE_results = ReCIDE_deconvolution(Sig_list = ref_MGM.list,
                                      EXP_df = EXP_df,
                                      Method = 'DWLS',
                                      n_cores = 50,
                                      n_celltype = 0)

Sig_list: list of reference MGMs output in step 3

EXP_df: bulk expression matrix (row: gene, col: subject) ，data.frame，Same as in step 3

Method: Deconvolution kernel, can be DWLS, CIBERSORT or FARDEEP, default is DWLS

n_cores: Number of parallel cores, default 5

n_celltype: Subjects in the reference set with a cell type number greater than n_celltype are used for deconvolution to prevent a situation where a cell type number is too low for a particular reference subject. The default is 0.

The final deconvolution results are stored in ReCIDE_results[["results_final_df"], where Rowname is the cell type and Colname is the subject name.

ReCIDE_results[["results_final_df"]]