[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

R/BASE.R

@@ -121,7 +121,6 @@ base_algorithm <- function(data, reg, perm = 100, median.norm = T) {
 }
 
 
-
 #' Souce code to create the compendium used kin the BASE algorithm, containing
 #'    up- and down-regulated weight sets that specify the
 # 	    specificity by which each gene is expressed by a given cell.

R/custom_deconvolution_methods.R

@@ -15,7 +15,6 @@
 NULL
 
 
-
 #' List of methods that support the use of a custom signature
 #'
 #' The available methods are
@@ -40,7 +39,6 @@ custom_deconvolution_methods <- c(
 ###########################################################################
 
 
-
 #' Deconvolute using CIBERSORT or CIBERSORT abs and a custom signature matrix.
 #'
 #' @param gene_expression_matrix a m x n matrix with m genes and n samples
@@ -127,8 +125,6 @@ deconvolute_epic_custom <- function(gene_expression_matrix, signature_matrix,
 }
 
 
-
-
 #' Deconvolute using ConsesnusTME and a custom signature matrix
 #'
 #' @param gene_expression_matrix a m x n matrix with m genes and n samples. Data

R/estimate.R

@@ -9,7 +9,6 @@
 #'
 
 
-
 #' ESTIMATE algortihm: This function computes stromal, immune, and ESTIMATE scores
 #' per sample using gene expression data, through GSEA. The ESTIMATE score is used
 #' to compute an estimate of the tumor purity

R/immune_deconvolution_methods.R

@@ -73,7 +73,6 @@ set_cibersort_mat <- function(path) {
 }
 
 
-
 ###########################################################################
 # Deconvolution functions for consistently accessing each method
 #
@@ -349,7 +348,6 @@ deconvolute_consensus_tme <- function(gene_expression_matrix,
 }
 
 
-
 #' Annotate unified cell_type names
 #'
 #' map the cell_types of the different methods to a common name

R/mouse_cell_type_mapping.R

@@ -54,8 +54,6 @@ reduce_mouse_cell_types <- function(deconvolution.results,
 }
 
 
-
-
 #' This function returns the list of all cell types in BASE/DCQ results,
 #'    along with the cell type they are mapped to
 #' @param method the method for which we want to get the cell types

R/mouse_deconvolution_methods.R

@@ -34,8 +34,6 @@ deconvolution_methods_mouse <- c(
 )
 
 
-
-
 ############################################################################
 # Deconvolution functions for consistently accessing each method
 #
@@ -45,7 +43,6 @@ deconvolution_methods_mouse <- c(
 ############################################################################
 
 
-
 #' Deconvolute using mMCP-counter
 #'
 #' @param gene_expression_matrix a m x n matrix with m genes and n samples. Should be TPM normalized
@@ -202,7 +199,6 @@ deconvolute_base_algorithm <- function(gene_expression_matrix, n_permutations =
 }
 
 
-
 #' Perform deconvolution on a mouse RNAseq dataset
 #'
 #' @param gene_expression_matrix a m x n matrix with m genes and n samples.

tests/testthat/test_custom_matrices.R

@@ -4,7 +4,6 @@ test_mat <- read_tsv("bulk_mat.tsv") %>%
 test_mat <- as.matrix(test_mat)
 
 
-
 test_that("seqimmucc works with a custom signature matrix", {
   test_mat <- dataset_racle$expr_mat
   sign_mat <- matrix(120 * runif(15000), ncol = 10)
@@ -22,7 +21,6 @@ test_that("seqimmucc works with a custom signature matrix", {
 })
 
 
-
 test_that("EPIC works with a custom signature matrix", {
   sign_mat <- matrix(120 * runif(10 * nrow(test_mat)), ncol = 10)
   colnames(sign_mat) <- c(
@@ -72,8 +70,6 @@ test_that("EPIC works with a custom signature matrix and variances", {
 })
 
 
-
-
 test_that("ConsensusTME works with a custom signature matrix", {
   sign_mat <- matrix(120 * runif(1500), ncol = 10)
   colnames(sign_mat) <- c(