Description Usage Arguments Value Examples
This function is used for testing differential gene expression.
1 2 3 | test_mean_main(treatment_data, treatment_data_weight, control_data,
control_data_weight, num_permute = 1000, ncore = 1,
log_transform = TRUE)
|
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 |
ncore |
Number of CPU cores used in the test |
log_transform |
If TRUE, take log2 transformation after adding a pseudo count of 1 to the input data. Default TRUE. |
statistics |
The weighted t-statistics |
pval_greater |
P-values for testing whether the expression of each gene in the treatment group is larger than that in the control group |
fdr_greater |
Adjusted p-values (FDR) of pval_greater |
pval_less |
P-values for testing whether the expression of each gene in the treatment group is smaller than that in the control group |
fdr_less |
Adjusted p-values (FDR) of pval_less |
pval_ts |
P-values for testing whether the expression of each gene in the treatment group is not equal to that in the control group |
fdr_ts |
Adjusted p-values (FDR) of pval_ts |
1 2 3 4 5 | ## Not run:
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)
## End(Not run)
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