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refactor: set default user params as function args
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@kelly-sovacool
kelly-sovacool committed Jul 31, 2024
1 parent 1789b4f commit e4e69a3
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Showing 2 changed files with 45 additions and 79 deletions.
119 changes: 41 additions & 78 deletions R/filter.R
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,38 @@
#' renee_ds2 <- filter_counts(renee_ds)
#' head(renee_ds2@counts[["filt"]])
#'
filter_counts <- function(renee_ds) {
filter_counts <- function(renee_ds,
gene_names_column = "Gene",
sample_names_column = "Sample",
groups_column = "Group",
labels_column = "Label",
columns_to_include = c("Gene", "A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3"),
outlier_samples_to_remove = c(),
use_cpm_counts_to_filter = TRUE,
Minimum_Count_Value_to_be_Considered_Nonzero = 8,
Minimum_Number_of_Samples_with_Nonzero_Counts_in_Total = 7,
Use_Group_Based_Filtering = FALSE,
Minimum_Number_of_Samples_with_Nonzero_Counts_in_a_Group = 3,
principal_component_on_x_axis = 1,
principal_component_on_y_axis = 2,
legend_position_for_pca = "top",
point_size_for_pca = 1,
add_labels_to_pca = TRUE,
label_font_size = 3,
label_offset_y_ = 2,
label_offset_x_ = 2,
samples_to_rename_manually = c(""),
color_histogram_by_group = FALSE,
set_min_max_for_x_axis_for_histogram = FALSE,
minimum_for_x_axis_for_histogram = -1,
maximum_for_x_axis_for_histogram = 1,
legend_position_for_histogram = "top",
legend_font_size_for_histogram = 10,
number_of_histogram_legend_columns = 6,
colors_for_plots = c("indigo", "carrot", "lipstick", "turquoise", "lavender", "jade", "coral", "azure", "green", "rum", "orange", "olive"),
number_of_image_rows = 2,
interactive_plots = FALSE,
plot_correlation_matrix_heatmap = TRUE) {
counts_matrix <- renee_ds@counts[["raw"]]
sample_metadata <- renee_ds@sample_meta
## --------- ##
Expand All @@ -43,78 +74,14 @@ filter_counts <- function(renee_ds) {
library(ComplexHeatmap)
library(ggrepel)

## -------------------------------- ##
## User-Defined Template Parameters ##
## -------------------------------- ##

# TODO move all user-defined parameters to function arguments
# TODO we should use "feature" instead of "gene" to make sure this is applicable beyond RNA-seq
# Basic Parameters:
gene_names_column <- "Gene"

columns_to_include <- c("Gene", "A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")
sample_names_column <- "Sample"
groups_column <- "Group"
labels_column <- "Label"


# Filtering Parameters:
outlier_samples_to_remove <- c()
use_cpm_counts_to_filter <- TRUE
Minimum_Count_Value_to_be_Considered_Nonzero <- 8
Minimum_Number_of_Samples_with_Nonzero_Counts_in_Total <- 7
Use_Group_Based_Filtering <- FALSE
Minimum_Number_of_Samples_with_Nonzero_Counts_in_a_Group <- 3

# PCA Parameters:
principal_component_on_x_axis <- 1
principal_component_on_y_axis <- 2
legend_position_for_pca <- "top"
point_size_for_pca <- 1
add_labels_to_pca <- TRUE
label_font_size <- 3
label_offset_y_ <- 2
label_offset_x_ <- 2
samples_to_rename_manually <- c("")

# Histogram Parameters:
color_histogram_by_group <- FALSE
set_min_max_for_x_axis_for_histogram <- FALSE
minimum_for_x_axis_for_histogram <- -1
maximum_for_x_axis_for_histogram <- 1
legend_position_for_histogram <- "top"
legend_font_size_for_histogram <- 10
number_of_histogram_legend_columns <- 6


# Visualization Parameters:
colors_for_plots <- c("indigo", "carrot", "lipstick", "turquoise", "lavender", "jade", "coral", "azure", "green", "rum", "orange", "olive")
number_of_image_rows <- 2
interactive_plots <- FALSE

# TCGA:
plot_correlation_matrix_heatmap <- TRUE

## --------------- ##
## Error Messages ##
## -------------- ##


## --------- ##
## Functions ##
## --------- ##

# TODO: just have users specify hex values directly for simplicity
colorlist <- c(
"#5954d6", "#e1562c", "#b80058",
"#00c6f8", "#d163e6", "#00a76c",
"#ff9287", "#008cf9", "#006e00",
"#796880", "#FFA500", "#878500"
)
names(colorlist) <- c(
"indigo", "carrot", "lipstick",
"turquoise", "lavender", "jade",
"coral", "azure", "green",
"rum", "orange", "olive"
indigo = "#5954d6", carrot = "#e1562c", lipstick = "#b80058",
turquoise = "#00c6f8", lavender = "#d163e6", jade = "#00a76c",
coral = "#ff9287", azure = "#008cf9", green = "#006e00", rum = "#796880",
orange = "#FFA500", olive = "#878500"
)
if (length(colors_for_plots) == 0) {
colors_for_plots <- c(
Expand All @@ -125,10 +92,6 @@ filter_counts <- function(renee_ds) {
)
}


## --------------- ##
## Main Code Block ##
## --------------- ##
# purpose of this code block for samples_to_include:
# ensure samples in metadata match columns in counts table and
# also exclude annotation / gene columns
Expand Down Expand Up @@ -167,6 +130,7 @@ filter_counts <- function(renee_ds) {
counts_matrix = df,
sample_metadata = sample_metadata,
gene_names_column = gene_names_column,
groups_column = groups_column,
use_cpm_counts_to_filter = use_cpm_counts_to_filter,
Use_Group_Based_Filtering = Use_Group_Based_Filtering,
Minimum_Count_Value_to_be_Considered_Nonzero = Minimum_Count_Value_to_be_Considered_Nonzero,
Expand Down Expand Up @@ -239,8 +203,6 @@ filter_counts <- function(renee_ds) {
grid.newpage()
# print(histPlot2)
} else {
## & Function Start
# gh<-plot_heatmap(df.filt[,samples_to_include],sample_metadata,colorval)
corHM <- plot_heatmap(
counts_matrix = df.filt[, samples_to_include],
sample_metadata = sample_metadata,
Expand All @@ -249,7 +211,6 @@ filter_counts <- function(renee_ds) {
anno_column = groups_column,
anno_colors = colorval
)
## & Function End

# grid.newpage()
# print(pcaPlot)
Expand Down Expand Up @@ -302,7 +263,9 @@ filter_counts <- function(renee_ds) {
#' @return counts matrix with low-count genes removed
#' @keywords internal
#'
remove_low_count_genes <- function(counts_matrix, sample_metadata, gene_names_column,
remove_low_count_genes <- function(counts_matrix, sample_metadata,
gene_names_column,
groups_column,
use_cpm_counts_to_filter = TRUE,
Use_Group_Based_Filtering = FALSE,
Minimum_Count_Value_to_be_Considered_Nonzero = 8,
Expand Down
5 changes: 4 additions & 1 deletion tests/testthat/test-filter.R
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@ test_that("filter_counts reproduces NIDAP results", {
renee_ds <- create_reneeDataSet_from_dataframes(
as.data.frame(nidap_sample_metadata),
as.data.frame(nidap_clean_raw_counts),
sample_id_colname = Sample
sample_id_colname = "Sample"
)
set.seed(10)
renee_ds2 <- filter_counts(renee_ds)
Expand Down Expand Up @@ -57,6 +57,7 @@ test_that("remove_low_count_genes works", {
counts_matrix = df,
sample_metadata = sample_meta,
gene_names_column = "Gene",
groups_column = "Group",
use_cpm_counts_to_filter = TRUE,
Use_Group_Based_Filtering = FALSE,
Minimum_Count_Value_to_be_Considered_Nonzero = 8,
Expand Down Expand Up @@ -138,6 +139,7 @@ test_that("remove_low_count_genes works", {
counts_matrix = df,
sample_metadata = sample_meta,
gene_names_column = "Gene",
groups_column = "Group",
use_cpm_counts_to_filter = TRUE,
Use_Group_Based_Filtering = TRUE,
Minimum_Count_Value_to_be_Considered_Nonzero = 8,
Expand Down Expand Up @@ -238,6 +240,7 @@ test_that("remove_low_count_genes works", {
counts_matrix = df,
sample_metadata = sample_meta,
gene_names_column = "Gene",
groups_column = "Group",
use_cpm_counts_to_filter = TRUE,
Use_Group_Based_Filtering = FALSE,
Minimum_Count_Value_to_be_Considered_Nonzero = -1,
Expand Down

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