scdv_main: Differential hyper-variability test

In WeiqiangZhou/SCDV: Single cell differential variance analysis

Description

This function is used for testing differential hyper-variability.

Usage

scdv_main(treatment_data, treatment_data_weight, control_data,
  control_data_weight, num_permute = 1000, span_param = 0.5, ncore = 1)

Arguments

treatment_data	Normalized count data for the treatment group
treatment_data_weight	1 - dropout probability for the treatment group
control_data	Normalized count data for the control group
control_data_weight	1 - dropout probability for the control group
num_permute	Number of permutation performed in the test
span_param	The span parameter in loess when fitting the mean-variance curve
ncore	Number of CPU cores used in the test

Value

sf_treatment	The hyper-variability statistics for the treatment group
sf_control	The hyper-variability statistics for the control group
sf_diff	Difference between sf_treatment and sf_control: sf_treatment - sf_control
sf_diff_pval	P-values for testing whether the hyper-variability of each gene in the treatment group is larger than that in the control group
sf_diff_fdr	Adjusted p-values (FDR) of sf_diff_pval
sf_diff_pval_alt	P-values for testing whether the hyper-variability of each gene in the treatment group is smaller than that in the control group
sf_diff_fdr_alt	Adjusted p-values (FDR) of sf_diff_pval_alt
sf_diff_pval_ts	P-values for testing whether the hyper-variability of each gene in the treatment group is not equal to that in the control group
sf_diff_fdr_ts	Adjusted p-values (FDR) of sf_diff_pval_ts

Examples

## Not run: 
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)

## End(Not run)

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