@@ -58,7 +58,7 @@ Collate:

     'compute_metrics.R'

     'count_mapped_reads.R'

     'count_table.R'

-    'create_edger_obj.R'

+    'create_screenr_obj.R'

     'create_screenr_object.R'

     'distribution_mapped_reads.R'

     'filter_by.R'

deleted file mode 100644

@@ -1,31 +0,0 @@

-#' @title Create edgeR Object

-#' @description Utility function that using the screenr-class

-#'              object create the corresponding edgeR object.

-#'              This function and other utility function enables the user to

-#'              not worry abut the implementation and just focus

-#'              on the analysis. The ScreenR package will take care of the rest.

-#' @param screenR_Object The ScreenR object obtained using the

-#'                       \code{\link{create_screenr_object}}

-#' @importFrom  edgeR DGEList

-#' @return The edgeR object will all the needed information for the analysis.

-#' @concept objects

-#' @export

-#' @examples

-#' object <- get0("object", envir = asNamespace("ScreenR"))

-#' create_edger_obj(object)

-create_edger_obj <- function(screenR_Object) {

-    # First create the Matrix of the Count table

-    counts <- screenR_Object@normalized_count_table

-    counts <- as.matrix(dplyr::select_if(counts, is.numeric))

-    # The group for the treatment

-    groups <- screenR_Object@groups

-

-    # The annotation

-    genes <- screenR_Object@annotation_table

-

-    # Create the edgeR object

-    DGEList <- edgeR::DGEList(counts = counts,

-                              group = groups,

-                              genes = genes)

-    return(DGEList)

-}

similarity index 100%

rename from R/create_ScreenR_object.R

rename to R/create_screenR_object.R

similarity index 100%

rename from R/create_edgeR_obj.R

rename to R/create_screenr_obj.R

deleted file mode 100644

@@ -1,163 +0,0 @@

-#' @title Multidimensional Scaling Plot

-#' @description Plot samples on a two-dimensional scatterplot so that

-#'              distances on the plot approximate the typical log2 fold

-#'              changes between the samples.

-#' @param screenR_Object The Object of the package

-#'                       \code{\link{create_screenr_object}}

-#' @param groups The vector that has to be used to fill the plot if NULL the

-#'               function will use the default groups slot in the object passed

-#'               as input.

-#' @param alpha The opacity of the labels.

-#'              Possible value are in a range from 0 to 1.

-#' @param size The dimension of the labels. The default value is 2.5

-#' @param color The color of the labels. The default value is black

-#' @importFrom ggplot2 geom_label labs

-#' @importFrom ggplot2 scale_y_continuous

-#' @importFrom limma plotMDS

-#' @return The MDS Plot

-#' @export

-#' @examples

-#' object <- get0("object", envir = asNamespace("ScreenR"))

-#'

-#' plot_mds(object)

-plot_mds <- function(screenR_Object, groups = NULL, alpha = 0.8, size = 2.5,

-    color = "black") {

-    # We have to convert the screenR obj into an edgeR obj

-    DGEList <- create_edger_obj(screenR_Object)

-

-    # The Standard plotMDS

-    plotMDS <- limma::plotMDS(DGEList, plot = FALSE, ndim = 2)

-

-    # Create the Updated plot MDS

-    PLTdata <- data.frame(

-        Sample = rownames(plotMDS$distance.matrix.squared),

-        x = plotMDS$x, y = plotMDS$y

-    )

-

-    if (is.null(groups)) {

-        PLTdata$group <- DGEList$samples$group

-    } else {

-        PLTdata$group <- groups

-    }

-

-    plot <- ggplot2::ggplot(PLTdata, aes(

-        x = .data$x, y = .data$y,

-        fill = .data$group

-    )) +

-        ggplot2::geom_label(aes(label = .data$Sample),

-            color = color, size = size, alpha = alpha

-        ) +

-        ggplot2::labs(x = "First Dimension", y = "Second Dimension")

-

-    return(plot)

-}

-

-#' @title Plot the explained variance by the PC

-#' @description This function plot the explained variance by the

-#'              Principal Component analysis.

-#' @param screenR_Object The ScreenR object obtained using the

-#'                       \code{\link{create_screenr_object}}

-#' @param cumulative A boolean value which indicates whether or not to plot

-#'                   the cumulative variance. The default value is FALSE.

-#' @param color The color to fill the barplot the default value is steelblue

-#' @importFrom  scales percent

-#' @return The explained variance plot

-#' @export

-#' @examples

-#' object <- get0("object", envir = asNamespace("ScreenR"))

-#'

-#' plot_explained_variance(object)

-#'

-#' # For the cumulative plot

-#' plot_explained_variance(object, cumulative = TRUE)

-plot_explained_variance <- function(screenR_Object, cumulative = FALSE,

-    color = "steelblue") {

-    PC <- compute_explained_variance(screenR_Object)

-    # Remove the Standard deviation row

-    PC <- filter(PC, .data$Name != "Standard deviation")

-

-    # Get only the numeric columns which corresponds to the PCs

-    numeric_col <- colnames(PC[, unlist(lapply(PC, is.numeric))])

-

-    # Transform the data in a longer format

-    PC <- tidyr::pivot_longer(

-        data = PC, cols = all_of(numeric_col),

-        names_to = "name"

-    )

-    PC <- dplyr::mutate(PC, name = factor(

-        x = .data$name,

-        levels = unique(.data$name)

-    ))

-    plot <- NULL

-

-

-    if (cumulative) {

-        # Select only the Cumulative Proportion

-        PC <- dplyr::filter(PC, .data$Name == "Cumulative Proportion")

-

-        plot <- ggplot2::ggplot(PC, aes(x = .data$name, y = .data$value)) +

-            geom_bar(stat = "identity", fill = color, col = "black") +

-            geom_point() +

-            geom_line(aes(group = .data$Name)) +

-            scale_y_continuous(labels = scales::percent) +

-            labs(

-                x = NULL,

-                y = "Cumulative Expressed Variance (%)"

-            )

-    } else {

-        # Select only the Proportion of Variance

-        PC <- dplyr::filter(PC, .data$Name == "Proportion of Variance")

-

-        plot <- ggplot2::ggplot(PC, aes(x = .data$name, y = .data$value)) +

-            geom_bar(stat = "identity", fill = color, col = "black") +

-            geom_point() +

-            geom_line(aes(group = .data$Name)) +

-            scale_y_continuous(labels = percent) +

-            labs(x = NULL, y = "Expressed Variance (%)")

-    }

-

-    return(plot)

-}

-

-#' @title Compute explained variance

-#' @description This  is an internal function  used to compute

-#'              the explained variance by each of the Principal Components.

-#' @importFrom stats  prcomp

-#' @param screenR_Object The Object of the package

-#' @return A data.frame containing all the information of the variance

-#'         expressed by the components

-#' @keywords internal

-#' @export

-#' @examples

-#' object <- get0("object", envir = asNamespace("ScreenR"))

-#'

-#' compute_explained_variance(object)

-compute_explained_variance <- function(screenR_Object) {

-    data <- screenR_Object@count_table

-    # Get only the numeric columns

-    numeric_col <- unlist(lapply(data, is.numeric))

-

-    # The data for the PC corresponds only on the numeric column

-    data_PC <- data[, numeric_col]

-

-    # To compute the PC the features has to be columns

-    data_PC <- t(data_PC)

-

-    # Rename the columns

-    colnames(data_PC) <- data$Barcode

-

-    # Computing the PCS

-    PC <- prcomp(data_PC)

-

-    # Extract the importance ad create a data.frame

-    PC <- data.frame(summary(PC)$importance)

-

-    PC <- tibble::rownames_to_column(PC, var = "Name")

-

-    PC <- dplyr::mutate(PC, Name = factor(

-        x = .data$Name,

-        levels = unique(.data$Name)

-    ))

-

-    return(PC)

-}

deleted file mode 100644

@@ -1,33 +0,0 @@

-#' @title Plot distribution Z-score

-#' @description This function plots the Log2FC Z-score distribution of the

-#'              treated vs control in the different time points.

-#'

-#' @param time_point_measure A list containing the table for each time

-#'                           point. Each table contains for each barcode

-#'                           the counts for the treated and control the Log2FC,

-#'                           Zscore, ZscoreRobust, Day.

-#'

-#' @param alpha A value for the opacity of the plot.

-#'              Allowed values are in the range 0 to 1

-#' @importFrom rlang .data

-#' @return return the density plot of the distribution of the Z-score

-#' @export

-#' @examples

-#' object <- get0("object", envir = asNamespace("ScreenR"))

-#'

-#' table1 <- compute_metrics(object,

-#'     control = "TRT", treatment = "Time3",

-#'     day = "Time3"

-#' )

-#'

-#' table2 <- compute_metrics(object,

-#'     control = "TRT", treatment = "Time4",

-#'     day = "Time4"

-#' )

-#'

-#' plot_zscore_distribution(list(table1, table2), alpha = 0.5)

-plot_zscore_distribution <- function(time_point_measure, alpha = 1) {

-    dplyr::bind_rows(time_point_measure) %>%

-        ggplot(aes(x = .data$Log2FC, fill = .data$Day)) +

-        geom_density(alpha = alpha)

-}

@@ -161,3 +161,7 @@ compute_explained_variance <- function(screenR_Object) {

     return(PC)

 }

+

+

+

+

deleted file mode 100644

@@ -1,265 +0,0 @@

-#' @include  generics.R

-

-#' @title S4 ScreenR object class

-#' The screenr_object class is the main object of the package, it is passed

-#' to a series of function to perform the analysis.

-#'

-#' @slot count_table It is used to store the count table to perform the

-#'                   analysis

-#' @slot annotation_table It is used to store the annotation of the shRNA

-#' @slot groups It is used to store the vector of treated and untreated

-#' @slot replicates It is used to store information about the replicates

-#' @slot normalized_count_table It is used to store a normalized version of

-#'                              the count table

-#' @slot data_table It is used to store a tidy format of the count table

-#' @exportClass screenr_object

-#' @concept objects

-#' @rdname get_count_table

-#' @examples

-#' data("count_table", package = "ScreenR")

-#' data("annotation_table", package = "ScreenR")

-#'

-#' groups <- factor(c(

-#'     "T1/T2", "T1/T2", "Treated", "Treated", "Treated",

-#'     "Control", "Control", "Control", "Treated", "Treated",

-#'     "Treated", "Control", "Control", "Control"

-#' ))

-#'

-#' obj <- create_screenr_object(

-#'     table = count_table,

-#'     annotation = annotation_table,

-#'     groups = groups,

-#'     replicates = c("")

-#' )

-screenr_object <- setClass("screenr_object", methods::representation(

-    count_table = "data.frame",

-    annotation_table = "data.frame", groups = "factor", replicates = "vector",

-    normalized_count_table = "data.frame", data_table = "data.frame"

-))

-

-

-

-

-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

-#                                 S4 methods

-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

-

-#' @rdname get_count_table

-#' @aliases get_count_table,screenr_object

-#' @export

-setMethod(

-    f = "get_count_table",

-    signature = "screenr_object",

-    definition = function(object) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        count_table <- slot(object = object, name = "count_table")

-        cat(

-            "ScreenR count table containing:\n",

-            nrow(x = count_table), "rows\n",

-            ncol(x = count_table), "columns\n"

-        )

-        return(count_table)

-    }

-)

-

-

-#' @rdname get_annotation_table

-#' @aliases get_annotation_table,screenr_object

-#' @export

-setMethod(

-    f = "get_annotation_table",

-    signature = "screenr_object",

-    definition = function(object) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        annotation_table <- slot(object = object, name = "annotation_table")

-        cat(

-            "ScreenR annotation table containing:\n",

-            nrow(annotation_table),

-            "rows\n",

-            ncol(annotation_table),

-            "columns\n"

-        )

-        return(annotation_table)

-    }

-)

-

-

-#' @export

-#' @aliases get_groups,screenr_object

-#' @rdname get_groups

-setMethod(

-    f = "get_groups",

-    signature = "screenr_object",

-    definition = function(object) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        return(slot(object = object, name = "groups"))

-    }

-)

-

-

-#' @export

-#' @aliases get_replicates,screenr_object

-#' @rdname get_replicates

-setMethod(

-    f = "get_replicates",

-    signature = "screenr_object",

-    definition = function(object) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        return(slot(object = object, name = "replicates"))

-    }

-)

-

-

-#' @export

-#' @aliases get_normalized_count_table,screenr_object

-#' @rdname get_normalized_count_table

-setMethod(

-    f = "get_normalized_count_table",

-    signature = "screenr_object",

-    definition = function(object) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        normalized_count_table <-

-            slot(object = object, name = "normalized_count_table")

-        cat(

-            "ScreenR normalized count table containing:\n",

-            nrow(x = normalized_count_table), "rows\n",

-            ncol(x = normalized_count_table), "columns\n"

-        )

-        return(normalized_count_table)

-    }

-)

-

-

-#' @export

-#' @aliases get_data_table,screenr_object

-#' @rdname get_data_table

-setMethod(

-    f = "get_data_table",

-    signature = "screenr_object",

-    definition = function(object) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        data_table <-

-            slot(object = object, name = "data_table")

-        cat(

-            "ScreenR normalized data table containing:\n",

-            nrow(x = data_table), "rows\n",

-            ncol(x = data_table), "columns\n"

-        )

-        return(data_table)

-    }

-)

-

-

-#' @export

-#' @aliases set_count_table,screenr_object

-#' @rdname set_count_table

-setMethod(

-    f = "set_count_table",

-    signature = "screenr_object",

-    definition = function(object, count_table) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        slot(object = object, name = "count_table") <- count_table

-        return(object)

-    }

-)

-

-

-#' @export

-#' @aliases set_annotation_table,screenr_object

-#' @rdname set_annotation_table

-setMethod(

-    f = "set_annotation_table",

-    signature = "screenr_object",

-    definition = function(object, annotation_table) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        slot(object = object, name = "annotation_table") <- annotation_table

-        return(object)

-    }

-)

-

-

-#' @export

-#' @aliases set_groups,screenr_object

-#' @rdname set_groups

-setMethod(

-    f = "set_groups",

-    signature = "screenr_object",

-    definition = function(object, groups) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        slot(object = object, name = "groups") <- groups

-        return(object)

-    }

-)

-

-

-

-#' @export

-#' @aliases set_replicates,screenr_object

-#' @rdname set_replicates

-setMethod(

-    f = "set_replicates",

-    signature = "screenr_object",

-    definition = function(object, replicates) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        slot(object = object, name = "replicates") <- replicates

-        return(object)

-    }

-)

-

-

-#' @export

-#' @aliases set_normalized_count_table,screenr_object

-#' @rdname set_normalized_count_table

-setMethod(

-    f = "set_normalized_count_table",

-    signature = "screenr_object",

-    definition = function(object, normalized_count_table) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        slot(

-            object = object,

-            name = "normalized_count_table"

-        ) <- normalized_count_table

-        return(object)

-    }

-)

-

-

-#' @export

-#' @aliases set_data_table,screenr_object

-#' @rdname set_data_table

-setMethod(

-    f = "set_data_table",

-    signature = "screenr_object",

-    definition = function(object, data_table) {

-        if (is.null(object)) {

-            stop("The object is not defined!")

-        }

-        slot(

-            object = object,

-            name = "data_table"

-        ) <- data_table

-        return(object)

-    }

-)

deleted file mode 100644

@@ -1,27 +0,0 @@

-% Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/create_edger_obj.R

-\name{create_edger_obj}

-\alias{create_edger_obj}

-\title{Create edgeR Object}

-\usage{

-create_edger_obj(screenR_Object)

-}

-\arguments{

-\item{screenR_Object}{The ScreenR object obtained using the

-\code{\link{create_screenr_object}}}

-}

-\value{

-The edgeR object will all the needed information for the analysis.

-}

-\description{

-Utility function that using the screenr-class

-             object create the corresponding edgeR object.

-             This function and other utility function enables the user to

-             not worry abut the implementation and just focus

-             on the analysis. The ScreenR package will take care of the rest.

-}

-\examples{

-object <- get0("object", envir = asNamespace("ScreenR"))

-create_edger_obj(object)

-}

-\concept{objects}

@@ -1,5 +1,5 @@

 % Generated by roxygen2: do not edit by hand

-% Please edit documentation in R/create_edger_obj.R

+% Please edit documentation in R/create_screenr_obj.R

 \name{create_edger_obj}

 \alias{create_edger_obj}

 \title{Create edgeR Object}