README.md

In WeiqiangZhou/SCDV: Single cell differential variance analysis

SCDV: Single cell differential variance analysis

Overview

SCDV is an R package for analyzing differentially variable genes in single-cell RNA-seq data.

Installation

To install SCDV via Github, run the following code in R:

if (!require("devtools"))
  install.packages("devtools")
devtools::install_github("WeiqiangZhou/SCDV")

Updates

05/06/2018: Updated differential mean test and differential var test by using the log-transformed data in the tests. 05/16/2018: Fix a bug in the calculation of two-sided p-values in differential var test.

How to use

There are three major steps for using SCDV

Step 1. Estimate dropout probability

library(SCDV)

##input your count data as data_treatment_count and data_control_count, rownames of the input matrix should be ensembl id
data_treatment_count <- input_data_treatment
data_control_count <- input_data_control

##get gene information
gene_info <- annotation_data[["gene_len_GRCh37"]] 
##change to "gene_info <- annotation_data[["gene_len_GRCm38"]]" for mouse data

match_gene_idx <- match(rownames(data_treatment_count),gene_info[,1])
gene_len <- gene_info[match_gene_idx,2]
names(gene_len) <- gene_info[match_gene_idx,1]
match_gene_name <- gene_info[match_gene_idx,-2]

##get neighboring cells
neighbor_result_treatment <- get_expected_cell(data_treatment_count,gene_len)
neighbor_result_control <- get_expected_cell(data_control_count,gene_len)

##estimate dropout probability, set multi-core using ncore, if not using multi-core, set ncore=1
output_treatment <- estimate_dropout_main(data_treatment_count,neighbor_result_treatment$data_expect,gene_len,ncore=6)
output_control <- estimate_dropout_main(data_control_count,neighbor_result_control$data_expect,gene_len,ncore=6)

save(output_treatment,file="output_treatment.rda")
save(output_control,file="output_control.rda")

Step 2. Adjust library size using house keeping genes

hp_gene <- annotation_data[["LV_gene_human"]][,1]
##change to "hp_gene <- annotation_data[["LV_gene_mouse"]][,1]" for mouse data

gene_names <- sapply(rownames(data_treatment_count),function(x) sub("\\..*","",x))
library_scale <- adjust_library_size(c(output_treatment,output_control),hp_gene,gene_names)

treatment_data_true <- sapply(output_treatment,function(x) x$data_true)
treatment_data_weight <- 1 - sapply(output_treatment,function(x) x$post_weight)

control_data_true <- sapply(output_control,function(x) x$data_true)
control_data_weight <- 1 - sapply(output_control,function(x) x$post_weight)

treatment_data_adjust <- t(t(treatment_data_true)*library_scale[1:length(output_treatment)])
control_data_adjust <- t(t(control_data_true)*library_scale[-c(1:length(output_treatment))])

Step 3. Perform differential analysis

SCDV supports three types of tests:

1. differential hyper-variability test

##set multi-core using ncore, if not using multi-core, set ncore=1
diff_disper <- scdv_main(treatment_data_adjust,treatment_data_weight,control_data_adjust,control_data_weight,num_permute=10000,span_param=0.5,ncore=6)
write.csv(cbind(match_gene_name,diff_disper),file="diff_hypervar.csv",row.names=FALSE)

2. differential variability test

##set multi-core using ncore, if not using multi-core, set ncore=1
diff_var <- test_var_main(treatment_data_adjust,treatment_data_weight,control_data_adjust,control_data_weight,num_permute=10000,ncore=6,log_transform=TRUE)
write.csv(data.frame(match_gene_name,diff_var),file="diff_var.csv",row.names=FALSE)

3. differential mean test

##set multi-core using ncore, if not using multi-core, set ncore=1
diff_expr <- test_mean_main(treatment_data_adjust,treatment_data_weight,control_data_adjust,control_data_weight,num_permute=10000,ncore=6,log_transform=TRUE)
write.csv(data.frame(match_gene_name,diff_expr),file="diff_expr.csv",row.names=FALSE)

Q&A

What's the assumption of the tests in SCDV?

All the tests in SCDV are permutation-based tests. These are non-parametric tests that do not rely on strong parametric assumptions.

What's the difference between hyper-variability test and variability test?

The major difference is they test different types of variance.

The variance of a gene in single-cell RNA-seq data has been showed to be highly correlated with the mean expression of the gene. To remove such "mean effects", SCDV calculates the hyper-variability statistics which is a ratio of the observed variance of a gene to the expected variance at the same mean expression level.

What are the return values of each test?

Use ?scdv_main, ?test_var_main, and ?test_mean_main in R to check the description of the return values in the help page of the package.

What is "house keeping genes"?

The "house keeping genes" are genes that show low variability but consistently expressed in different cell types which are obtained using recount2 (https://jhubiostatistics.shinyapps.io/recount/).

How to cite SCDV?

Use citation("SCDV") in R to get the citation information.

Contact

Please contact Weiqiang Zhou: [e-mail] for questions and suggestions.

WeiqiangZhou/SCDV documentation built on May 17, 2019, 12:04 p.m.