Description Usage Arguments Details Value See Also
The MODifieR input objects can be used in downstream analysis for the disease module inference methods included in this package.
1 2 3 4 | create_input_rnaseq(count_matrix, group1_indici, group2_indici,
group1_label, group2_label, expression = T,
differential_expression = T, use_adjusted = T,
normalize_quantiles = F)
|
count_matrix |
Matrix containing raw RNA-seq counts |
group1_indici |
vector containing indici for samples belonging to group 1 (Column numbers) |
group2_indici |
vector containing indici for samples belonging to group 2 (Column numbers) |
group1_label |
Label for each group 1, for example "patient" or "control" |
group2_label |
Label for each group 2, for example "patient" or "control" |
expression |
boolean, calculate expression values? |
differential_expression |
boolean, calculate differentially expressed data? |
use_adjusted |
boolean, use adjusted p value for differential expression analysis? |
normalize_quantiles |
boolean, Normalize quantiles for WGCNA-based methods? |
The function creates an input object to be used in all disease module inference methods. Differentially
expressed genes are calculated using generalized linear models from the edgeR
package. For WGCNA-based
methods raw counts are normalized using the varianceStabilizingTransformation
from the DESeq2
package.
Optionally, Quantile normalization using the normalize.quantiles
function from the preprocessCore
can be applied.
The function returns an object of class "MODifieR_input". The object is a named list containing the following components:
diff_genes |
A 2 two column data.frame where the first column are genes and the second column unadjusted p-values obtained by differential expression analysis |
edgeR_deg_table |
A data.frame from |
annotated_exprs_matrix |
A matrix where the rows are genes and the columns samples. Raw counts have been normalized
with |
count_matrix |
A matrix, the original input count matrix |
group_indici |
A named list containing 2 numeric vectors. The names are the group labels and the values are the group indici |
glmQLFit
varianceStabilizingTransformation
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