Commit 19666647 authored by Lars Velten's avatar Lars Velten
Browse files

updated documentation

parent 974a6bd7
......@@ -66,11 +66,11 @@ CIBERSORT <- function(response,features, transform, usegenes, norm=T, nu= c(0.25
#'@param design A named vector assigning sample names to sample class, see examples below.
#'@param markergenes A vector of genes to be included in the analysis, defaults to \code{intersect( rownames(mean_by_cluster), rownames(exprs) )}
#'@param transform A function to be applied to columns of \code{exprs} and \{base} following normalization. Defaults to no transformation since bulk RNA-seq profiles are generated by pooling up RNA from constituent cell types. In the original CIBERSORT paper, a logarithmic transform was used.
#'@param nu Different values of nu to evaluate support vector regression at, see \code{\link[e1071]svm}. Nu defines how many support vectors (i.e. genes) to use in regression.
#'@param nu Different values of nu to evaluate support vector regression at, see \code{\link{[e1071]svm}}. Nu defines how many support vectors (i.e. genes) to use in regression.
#'@param optim.nu In the original CIBERSORT implementation, SVR is evaluated at several values of nu and the value with the best RSME is chosen. This can lead to overfitting. If \code{optim.nu} is set to \code{TRUE}, the value for nu is chosen by cross validation, which leads to longer runtimes.
#'@param mc.cores Number of cores used, e.g. for the parallel evaluation at different balues of nu.
#'@param ... Parameters passed to \code{\link[e1071]svm}
#'@return
#'@return A data frame in long format suitable for plotting with ggplot2.
#'@examples
#'\dontrun{
#'#See also package vignette CIBERORT.Rmd
......
......@@ -42,11 +42,10 @@ RNAMagnetAnchors <- function(seurat, anchors, return = "summary", neighborhood.d
#'
#' RNAMagnet comes in two flavors: \code{RNAMagnetSignaling} and \code{\link{RNAMagnetAnchors}}. This function is meant to identify, for each cell type from a \code{\link[Seurat]{seurat}} object, potential signaling interactions with other cell types.
#'@param seurat An object of class \code{\link[Seurat]{seurat}} containing a valid clustering and t-SNE information. For information on how to create such an object, see https://satijalab.org/seurat/get_started.html
#'@param return Determines object to return; one of "summary" or "rnamagnet-class"
#'@param ... For explanation of all further parameters, see \code{\link{RNAMagnetBase}}.
#'@return Returns an objects of class \code{\link{rnamagnet}}. \code{\link{plotNetwork}} or \code{\link{plotInteraction}} can be used for further analyses.
#'@return Returns an objects of class \code{\link{rnamagnet}}. \code{\link{PlotSignalingNetwork}} or \code{\link{getRNAMagnetGenes}} can be used for further analyses.
#'@export
RNAMagnetSignaling <- function(seurat, return = "summary", neighborhood.distance = NULL, neighborhood.gradient = NULL, .k = 10, .x0 = 0.5, .minExpression = 10, .version = "latest", .cellularCompartment = c("Secreted","Both"), .manualAnnotation = "Correct" ) {
RNAMagnetSignaling <- function(seurat, neighborhood.distance = NULL, neighborhood.gradient = NULL, .k = 10, .x0 = 0.5, .minExpression = 10, .version = "latest", .cellularCompartment = c("Secreted","Both"), .manualAnnotation = "Correct" ) {
RNAMagnetBase(seurat, anchors = NULL, neighborhood.distance,neighborhood.gradient, .k, .x0, .minExpression, .version, .cellularCompartment, .manualAnnotation, FALSE)
......
......@@ -10,12 +10,10 @@
#' Meta data for LCM dataset
#'
#' @format Data frame, column \code{id} corresponds to the column names of \code{\link{NicheDataLCM}}. Other colummns describe various parameters related to potential sources of batch effects (e.g. the microscopy slide, the day of sample collection and processing, the sequencing lane and the size of the area sampled) and biology (Basic sample class, presence of sinusoids and distance from the endosteum)
#' @describeIn NicheDataLCM
#' @describeIn NicheDataLCM Data frame, column \code{id} corresponds to the column names of \code{\link{NicheDataLCM}}. Other colummns describe various parameters related to potential sources of batch effects (e.g. the microscopy slide, the day of sample collection and processing, the sequencing lane and the size of the area sampled) and biology (Basic sample class, presence of sinusoids and distance from the endosteum)
"NicheMetaDataLCM"
#' Markers for populations defined by 10x genomics
#'
#' @format Data frame of cell type specific markers - the output of running \code{FindMarkersAll{\link{NicheData10x}, method="roc"}}
#' @describeIn NicheData10x
#' @describeIn NicheData10x Data frame of cell type specific markers - the output of running \code{FindMarkersAll{\link{NicheData10x}, method="roc"}}
"NicheMarkers10x"
......@@ -4,7 +4,7 @@
\name{NicheMarkers10x}
\alias{NicheMarkers10x}
\title{Markers for populations defined by 10x genomics}
\format{Data frame of cell type specific markers - the output of running \code{FindMarkersAll{\link{NicheData10x}, method="roc"}}}
\format{An object of class \code{data.frame} with 10330 rows and 9 columns.}
\usage{
NicheMarkers10x
}
......
......@@ -4,7 +4,7 @@
\name{NicheMetaDataLCM}
\alias{NicheMetaDataLCM}
\title{Meta data for LCM dataset}
\format{Data frame, column \code{id} corresponds to the column names of \code{\link{NicheDataLCM}}. Other colummns describe various parameters related to potential sources of batch effects (e.g. the microscopy slide, the day of sample collection and processing, the sequencing lane and the size of the area sampled) and biology (Basic sample class, presence of sinusoids and distance from the endosteum)}
\format{An object of class \code{data.frame} with 76 rows and 10 columns.}
\usage{
NicheMetaDataLCM
}
......
......@@ -4,21 +4,19 @@
\alias{RNAMagnetSignaling}
\title{Runs RNAMagnet for identifying signaling interactions between cells}
\usage{
RNAMagnetSignaling(seurat, return = "summary",
neighborhood.distance = NULL, neighborhood.gradient = NULL,
.k = 10, .x0 = 0.5, .minExpression = 10, .version = "latest",
RNAMagnetSignaling(seurat, neighborhood.distance = NULL,
neighborhood.gradient = NULL, .k = 10, .x0 = 0.5,
.minExpression = 10, .version = "latest",
.cellularCompartment = c("Secreted", "Both"),
.manualAnnotation = "Correct")
}
\arguments{
\item{seurat}{An object of class \code{\link[Seurat]{seurat}} containing a valid clustering and t-SNE information. For information on how to create such an object, see https://satijalab.org/seurat/get_started.html}
\item{return}{Determines object to return; one of "summary" or "rnamagnet-class"}
\item{...}{For explanation of all further parameters, see \code{\link{RNAMagnetBase}}.}
}
\value{
Returns an objects of class \code{\link{rnamagnet}}. \code{\link{plotNetwork}} or \code{\link{plotInteraction}} can be used for further analyses.
Returns an objects of class \code{\link{rnamagnet}}. \code{\link{PlotSignalingNetwork}} or \code{\link{getRNAMagnetGenes}} can be used for further analyses.
}
\description{
RNAMagnet comes in two flavors: \code{RNAMagnetSignaling} and \code{\link{RNAMagnetAnchors}}. This function is meant to identify, for each cell type from a \code{\link[Seurat]{seurat}} object, potential signaling interactions with other cell types.
......
......@@ -13,7 +13,7 @@ getLigandsReceptors(version = "latest",
\item{version}{Currently supports the following values: \itemize{
\item{latest} points to \code{1.0.0}
\item{1.0.0} contains manual annotation for all genes expressed in bone marrow. This version was used for analysis is the Baccin et al paper.
\item{2.0.0} contains manual annotation for all genes in the geneome (TBD)
\item{2.0.0} contains manual annotation for all genes in the geneome (not yet complete)
\item Alternatively, a data frame with the same column names as \code{ligandsReceptors_1.0.0} can be used.
}}
......
......@@ -18,7 +18,7 @@ runCIBERSORT(exprs, base, design,
\item{markergenes}{A vector of genes to be included in the analysis, defaults to \code{intersect( rownames(mean_by_cluster), rownames(exprs) )}}
\item{nu}{Different values of nu to evaluate support vector regression at, see \code{\link[e1071]svm}. Nu defines how many support vectors (i.e. genes) to use in regression.}
\item{nu}{Different values of nu to evaluate support vector regression at, see \code{\link{[e1071]svm}}. Nu defines how many support vectors (i.e. genes) to use in regression.}
\item{optim.nu}{In the original CIBERSORT implementation, SVR is evaluated at several values of nu and the value with the best RSME is chosen. This can lead to overfitting. If \code{optim.nu} is set to \code{TRUE}, the value for nu is chosen by cross validation, which leads to longer runtimes.}
......@@ -26,6 +26,9 @@ runCIBERSORT(exprs, base, design,
\item{...}{Parameters passed to \code{\link[e1071]svm}}
}
\value{
A data frame in long format suitable for plotting with ggplot2.
}
\description{
This is a custom implementation of the algorithm described by Newman et al (Nautre Methods 12:453-457). CIBERSORT is an algorithm for estimating the cell type composition of a bulk sample, given a gene expression profile of the sample and a known gene expression profile for each cell type potentially contributing to the sample.
}
......
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