Last updated: 2022-07-15
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Knit directory: GSFA_analysis/
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CROP-seq datasets: /project2/xinhe/yifan/Factor_analysis/shared_data/ The data are Seurat objects, with raw gene counts stored in obj@assays$RNA@counts, and cell meta data stored in obj@meta.data. Normalized and scaled data used for GSFA are stored in obj@assays$RNA@scale.data , the rownames of which are the 6k genes used for GSFA.
MUSIC website: https://github.com/bm2-lab/MUSIC
setting
srun --mem=40G --time=10:0:0 --cpus-per-task=15Load packages
library(data.table)
library(Seurat)
library(MUSIC)
library(ggplot2)
theme_set(theme_bw() + theme(plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 13),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
panel.grid.minor = element_blank())
)
library(ComplexHeatmap)setwd("/project2/xinhe/kevinluo/GSFA/music_analysis/example")
dir.create("./output", recursive = TRUE, showWarnings = FALSE)expression_profile <- read.table("./crop_stimulated/expression_profile.txt",head=T,row.names=1,sep="\t")
perturb_information_df <- read.table("./crop_stimulated/perturb_information.txt",head=T,row.names=1,sep="\t")
perturb_information <- as.character(perturb_information_df[,1])
names(perturb_information) <- row.names(perturb_information_df)
# If you don't consider off-target effect, this file is not needed.
#sgRNA_information_df<-read.table("./crop_stimulated/sgRNA_information.txt",head=T,row.names=1,sep="\t")
#sgRNA_information<-as.character(sgRNA_information_df[,1])
#names(sgRNA_information)<-row.names(sgRNA_information_df)
# Have a look at expression_profile
dim(expression_profile) expression_profile[1:3,1:3]
# perturb_information.
length(perturb_information)setwd("/project2/xinhe/kevinluo/GSFA/music_analysis/example/output")# For "data_format_example/crop_unstimulated.RData", this function integrates the input data and filters mitochondrial ribosomal protein(^MRP) and ribosomal protein(^RP).
crop_seq_list <- Input_preprocess(expression_profile, perturb_information)
# For data format like "perturb_GSM2396857" generated by 10X genomics, function "Input_preprocess_10X()" will be suitable. Users can also change this data format to the standard format like "data_format_example/crop_unstimulated.RData", then use function "Input_preprocess()" to process it.
#crop_seq_list<-Input_preprocess_10X("./perturb_GSM2396857")
# cell quality control
crop_seq_qc <- Cell_qc(crop_seq_list$expression_profile, crop_seq_list$perturb_information, species="Hs", plot=F)
# data imputation, it may take a little long time without parallel computation.
crop_seq_imputation <- Data_imputation(crop_seq_qc$expression_profile, crop_seq_qc$perturb_information, cpu_num=15)
saveRDS(crop_seq_imputation, "output/crop_seq_imputation.rds")# cell filtering, it may take a little long time without parallel computation.
crop_seq_imputation <- readRDS("output/crop_seq_imputation.rds")
crop_seq_filtered <- Cell_filtering(crop_seq_imputation$expression_profile, crop_seq_imputation$perturb_information, cpu_num=10)
saveRDS(crop_seq_filtered, "output/crop_seq_filtered.rds")# obtain highly dispersion differentially expressed genes.
crop_seq_filtered <- readRDS("output/crop_seq_filtered.rds")
crop_seq_vargene <- Get_high_varGenes(crop_seq_filtered$expression_profile, crop_seq_filtered$perturb_information, plot=T)
saveRDS(crop_seq_vargene, "output/crop_seq_vargene.rds")# get topics.
crop_seq_vargene <- readRDS("output/crop_seq_vargene.rds")
system.time(
topic_model_list <- Get_topics(crop_seq_vargene$expression_profile,crop_seq_vargene$perturb_information,topic_number=c(4:6)))
saveRDS(topic_model_list, "output/topic_model_list.rds")
# This step may take a long time if you choosed a large scope of topic number. You can run each topic number seperately, then combine them to save time.
system.time(
topic_1 <- Get_topics(crop_seq_vargene$expression_profile,crop_seq_vargene$perturb_information,topic_number=4))
saveRDS(topic_1, "output/topic_4.rds")
system.time(
topic_2 <- Get_topics(crop_seq_vargene$expression_profile,crop_seq_vargene$perturb_information,topic_number=5))
saveRDS(topic_2, "output/topic_5.rds")
system.time(
topic_3 <- Get_topics(crop_seq_vargene$expression_profile,crop_seq_vargene$perturb_information,topic_number=6))
saveRDS(topic_3, "output/topic_6.rds")
topic_model_list<-list()
topic_model_list$models<-list()
topic_model_list$perturb_information<-topic_1$perturb_information
topic_model_list$models[[1]]<-topic_1$models[[1]]
topic_model_list$models[[2]]<-topic_2$models[[1]]
topic_model_list$models[[3]]<-topic_3$models[[1]]
saveRDS(topic_model_list, "output/topic_model_list.rds")
topic_model_list <- readRDS("output/topic_model_list.rds")
# select the optimal topic number.
optimalModel <- Select_topic_number(topic_model_list$models,plot=T)
# #If you just calculated one topic number, you can skip this step, just run the following:
# optimalModel <- topic_model_list$models[[1]]# annotate each topic's functions. For parameter "species", Hs(homo sapiens) or Mm(mus musculus) are available.
topic_func <- Topic_func_anno(optimalModel, species="Hs", plot=T)
saveRDS(topic_func, "output/topic_func.rds")# calculate topic distribution for each cell.
distri_diff <- Diff_topic_distri(optimalModel, topic_model_list$perturb_information, plot=T)
saveRDS(distri_diff, "output/distri_diff.rds")# calculate the overall perturbation effect ranking list without "offTarget_Info".
rank_overall_result <- Rank_overall(distri_diff)
saveRDS(rank_overall_result, "output/rank_overall_result.rds")
#rank_overall_result<-Rank_overall(distri_diff,offTarget_hash=offTarget_Info) (when "offTarget_Info" is available).
# calculate the topic-specific ranking list.
rank_topic_specific_result <- Rank_specific(distri_diff)
saveRDS(rank_topic_specific_result, "output/rank_topic_specific_result.rds")
# calculate the perturbation correlation.
perturb_cor <- Correlation_perturbation(distri_diff,plot=T)
saveRDS(perturb_cor, "output/perturb_cor.rds")
sessionInfo()