deg_deseq2 | R Documentation |
Used in the limma_value function to perform DEG analysis using the DESeq2 package. It is not recommended to use this function on its own.
deg_deseq2(
raw_counts,
max_p_limma = 0.05,
min_fc_limma = 2,
selected_comparisons = NULL,
sample_info = NULL,
model_factors = NULL,
block_factor = NULL,
reference_levels = NULL,
threshold_wald_test = FALSE,
independent_filtering = TRUE,
descr = ""
)
raw_counts |
The matrix of counts before processing for gene expression data |
max_p_limma |
Significant p-value to use for the fold-change values |
min_fc_limma |
Minimum fold-change to include in the results |
selected_comparisons |
Comparisons being analyzed in the DEG analysis |
sample_info |
Experiment file information for grouping |
model_factors |
Vector of selected factors and interaction terms from the model design |
block_factor |
The selected factors for batch effect |
reference_levels |
Vector of reference levels to use for the selected factors |
threshold_wald_test |
whether to use threshold-based Wald test to test null hypothesis that the absolute value of fold-change is bigger than a value. |
independent_filtering |
If yes(default), conduct independent filtering |
The return value is the results of the DEG analysis. These results are filtered and formatted by the limma_value function. results, a data frame with up or down regulated genes for all comparisons comparisons, a vectors holding comparison_names, exp_type, a character holding experimental design or error messages. top_genes, a list, each elements hold the lfc & FDR for a comparison
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.