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Tallulah Andrews
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‎DESCRIPTION‎

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Package: M3D
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Package: M3Drop
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Version: 1.0
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Date: 2016-02-10
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Title: Michaelis-Menten Modelling of Dropouts

‎NAMESPACE‎

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exportPattern("bg__*")
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exportPattern("M3D_*")
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exportPattern("M3Drop_*")
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export(Brennecke_getVariableGenes)
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import(RColorBrewer)
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import(gplots)

‎man/M3D_Clean_Data.Rd‎

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\name{M3D_Clean_Data}
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\alias{M3D_Clean_Data}
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\name{M3Drop_Clean_Data}
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\alias{M3Drop_Clean_Data}
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\alias{bg__filter_cells}
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\title{Filter Expression Data}
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\description{
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Filters and normalizes a given expression matrix. Removes low quality cells and undetected genes, and normalizes counts to counts per million.
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}
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\usage{
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M3D_Clean_Data(expr_mat, labels = NA, is.counts = TRUE, suppress.plot = FALSE, pseudo_genes = NA, min_detected_genes = NA)
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M3Drop_Clean_Data(expr_mat, labels = NA, is.counts = TRUE, suppress.plot = FALSE, pseudo_genes = NA, min_detected_genes = NA)
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bg__filter_cells(expr_mat, labels = NA, suppress.plot = FALSE, min_detected_genes = NA)
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}
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\arguments{
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\value{list with elements: data, the normalized filtered expression matrix; and labels, labels of the remaining cells.}
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\examples{
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library(M3DExampleData)
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cpm = M3D_Clean_Data(Mmus_example_list$data, Mmus_example_list$labels, is.counts=TRUE, min_detected_genes=2000) # Remove all cells with < 2000 detected genes and convert to cpm
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filtered_only = M3D_Clean_Data(Mmus_example_list$data, Mmus_example_list$labels, is.counts=FALSE) # Removes cells with significantly few detected genes (FDR=5%)
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cpm = M3Drop_Clean_Data(Mmus_example_list$data, Mmus_example_list$labels, is.counts=TRUE, min_detected_genes=2000) # Remove all cells with < 2000 detected genes and convert to cpm
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filtered_only = M3Drop_Clean_Data(Mmus_example_list$data, Mmus_example_list$labels, is.counts=FALSE) # Removes cells with significantly few detected genes (FDR=5%)
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}
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\keyword{normalization, quality control}

‎man/M3D_Differential_Expression.Rd‎

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\name{M3D_Differential_Expression}
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\alias{M3D_Differential_Expression}
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\name{M3Drop_Differential_Expression}
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\alias{M3Drop_Differential_Expression}
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\alias{bg__test_DE_K_equiv}
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\title{Differentially Expressed Genes.}
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\description{
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Use Michaelis-Menten curve to find differentially expressed (DE) genes.
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}
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\usage{
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M3D_Differential_Expression(expr_mat, mt_method="bon", mt_threshold=0.05, suppress.plot=FALSE)
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M3Drop_Differential_Expression(expr_mat, mt_method="bon", mt_threshold=0.05, suppress.plot=FALSE)
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bg__test_DE_K_equiv(expr_mat, fit=NA)
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}
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\arguments{
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}
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\details{Fits a Michaelis-Menten function to the dropout-rate (if not provided) of the provided expression matrix. Identifies genes where the gene-specific K calculated as (S = mean expression, P = dropout rate): \deqn{K=\frac{S*P}{1-P}}{K = (S * P)/(1 - P)} is significantly larger than the K fitted to the entire dataset. Combines standard errors of the fitted K, the gene-specific dropout rate and the gene-specific average expression using error propagation rules. Determines the signficance of the gene-specific K using a Z-test with the propagated error then applies the specified multiple testing correction to identify DE genes. Plots the dropout rate vs gene expression with the fitted MM curve and highlights in purple the significantly DE genes.}
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\value{
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\code{M3D_Differential Expression} : a data.frame of significantly differentially expressed genes with columns: Gene, p.value, q.value
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\code{M3Drop_Differential Expression} : a data.frame of significantly differentially expressed genes with columns: Gene, p.value, q.value
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\code{bg__test_DE_K_equiv} : named list of raw p-values (pval) and relative K size (fold_change) for all genes.
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}
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\examples{
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library(M3DExampleData)
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DE_genes = M3D_Differential_Expression(Mmus_example_list$data, suppress.plot=TRUE)
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DE_genes = M3D_Differential_Expression(Mmus_example_list$data)
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DE_genes = M3Drop_Differential_Expression(Mmus_example_list$data, suppress.plot=TRUE)
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DE_genes = M3Drop_Differential_Expression(Mmus_example_list$data)
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DE_test_results = bg__test_DE_K_equiv(Mmus_example_list$data);
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}
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\seealso{

‎man/M3D_Dropout_Models.Rd‎

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\name{M3D_Dropout_Models}
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\alias{M3D_Dropout_Models}
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\name{M3Drop_Dropout_Models}
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\alias{M3Drop_Dropout_Models}
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\title{Fit functions to the dropouts vs expression distribution.}
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\description{
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Fits the modified Michaelis-Menten equation (MM), a logistic regession (logistic), or a double exponential (ZIFA) function to the relationship between mean expression and dropout-rate (proportion of zero values).
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}
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\usage{M3D_Dropout_Models(expr_mat, xlim=NA, suppress.plot=FALSE)}
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\usage{M3Drop_Dropout_Models(expr_mat, xlim=NA, suppress.plot=FALSE)}
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\arguments{
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\item{expr_mat}{a numeric matrix of expression values, columns = samples, rows = genes.}
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\item{xlim}{limits for x-axis of plot.}
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\value{Invisibly, a list of output from each fit (MMfit, LogiFit, ExpoFit).}
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\examples{
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library(M3DExampleData)
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M3D_Dropout_Models(Mmus_example_list$data)
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M3Drop_Dropout_Models(Mmus_example_list$data)
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}
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\seealso{

‎man/M3D_Expression_Heatmap.Rd‎

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\name{M3D_Expression_Heatmap}
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\alias{M3D_Expression_Heatmap}
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\name{M3Drop_Expression_Heatmap}
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\alias{M3Drop_Expression_Heatmap}
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\alias{bg__expression_heatmap}
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\title{Plot Heatmap of Gene Expression}
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\description{
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Plots a customized heatmap of scaled log expression values.
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}
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\usage{
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M3D_Expression_Heatmap(genes, expr_mat, cell_labels=NA, interesting_genes=NA, key_genes=genes, key_cells=NA)
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M3Drop_Expression_Heatmap(genes, expr_mat, cell_labels=NA, interesting_genes=NA, key_genes=genes, key_cells=NA)
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bg__expression_heatmap(genes, expr_mat, cell_labels=NA, gene_labels=NA, key_genes=genes, key_cells=NA)
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}
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\arguments{
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\value{Invisibly, output from heatmap.2 call.}
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\examples{
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library(M3DExampleData)
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M3D_Expression_Heatmap(head(rownames(Mmus_example_list$data),20),Mmus_example_list$data, cell_labels = Mmus_example_list$labels)
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M3Drop_Expression_Heatmap(head(rownames(Mmus_example_list$data),20),Mmus_example_list$data, cell_labels = Mmus_example_list$labels)
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}
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\keyword{heatmap}

‎man/M3D_Get_Extremes.Rd‎

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\name{M3D_Get_Extremes}
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\alias{M3D_Get_Extremes}
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\name{M3Drop_Get_Extremes}
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\alias{M3Drop_Get_Extremes}
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\alias{bg__get_extreme_residuals}
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\title{Get outliers from MM curve.}
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\description{
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Identifies outliers left and right of a fitted Michaelis-Menten curve.
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}
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\usage{
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M3D_Get_Extremes(expr_mat, fdr_threshold=0.1, percent=NA, v_threshold=c(0.05,0.95), suppress.plot=FALSE)
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M3Drop_Get_Extremes(expr_mat, fdr_threshold=0.1, percent=NA, v_threshold=c(0.05,0.95), suppress.plot=FALSE)
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bg__get_extreme_residuals(expr_mat, fit=NA, v_threshold=c(0.05, 0.95), percent = NA, fdr_threshold = 0.1, direction="right", suppress.plot=FALSE)
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}
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\arguments{
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The normal distribution fit can be visualized by running \code{bg__get_extreme_residuals} with suppress.plot=FALSE.
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\code{M3D_Get_Extremes} identifies both left and right residuals using the provided thresholds in each direction. Eg. will return the \code{percent} smallest and \code{percent} largest residuals. It also plots the fitted MM curve and highlights the left and right extreme outliers unless suppress.plot=TRUE .
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\code{M3Drop_Get_Extremes} identifies both left and right residuals using the provided thresholds in each direction. Eg. will return the \code{percent} smallest and \code{percent} largest residuals. It also plots the fitted MM curve and highlights the left and right extreme outliers unless suppress.plot=TRUE .
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}
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\value{
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\code{M3D_Get_Extremes} List containing elements left and right, vectors of the names of the extreme genes to the left and right of the curve respectively.
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\code{M3Drop_Get_Extremes} List containing elements left and right, vectors of the names of the extreme genes to the left and right of the curve respectively.
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\code{bg__get_extreme_residuals} vector of names of the genes of the extreme genes.
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}
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\examples{
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library(M3DExampleData)
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extreme_gene_lists = M3D_Get_Extremes(Mmus_example_list$data, fdr_threshold=0.1, suppress.plot=TRUE)
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extreme_gene_lists = M3D_Get_Extremes(Mmus_example_list$data, percent=0.01) # plots fitted MM and highlights extreme genes.
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extreme_gene_lists = M3Drop_Get_Extremes(Mmus_example_list$data, fdr_threshold=0.1, suppress.plot=TRUE)
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extreme_gene_lists = M3Drop_Get_Extremes(Mmus_example_list$data, percent=0.01) # plots fitted MM and highlights extreme genes.
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R_extreme_genes = bg__get_extreme_residuals(Mmus_example_list$data, direction="right", fdr_threshold=0.05) #plots the fitted normal distribution
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}

‎man/M3D_Get_Heatmap_Cell_Clusters.Rd‎

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\name{M3D_Get_Heatmap_Cell_Clusters}
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\alias{M3D_Get_Heatmap_Cell_Clusters}
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\name{M3Drop_Get_Heatmap_Cell_Clusters}
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\alias{M3Drop_Get_Heatmap_Cell_Clusters}
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\title{Extracts cell clusters from heatmap output}
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\description{Extracts the clustering correponding to the given number of clusters from heatmap output.}
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\usage{M3D_Get_Heatmap_Cell_Clusters(heatmap_output, k)}
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\usage{M3Drop_Get_Heatmap_Cell_Clusters(heatmap_output, k)}
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\arguments{
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\item{heatmap_output}{Output from a gene-expression heatmap.}
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\item{k}{Number of clusters.}
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\value{A vector of cluster labels for each cell.}
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\examples{
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library(M3DExampleData)
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heatmap_out = M3D_Expression_Heatmap(rownames(Mmus_example_list$data)[1:20], Mmus_example_list$data)
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clusters = M3D_Get_Heatmap_Cell_Clusters(heatmap_out, k=5)
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heatmap_out = M3Drop_Expression_Heatmap(rownames(Mmus_example_list$data)[1:20], Mmus_example_list$data)
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clusters = M3Drop_Get_Heatmap_Cell_Clusters(heatmap_out, k=5)
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}
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\keyword{heatmap}

‎man/M3D_ThreeSet_Venn.Rd‎

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\name{M3D_ThreeSet_Venn}
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\alias{M3D_ThreeSet_Venn}
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\name{M3Drop_ThreeSet_Venn}
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\alias{M3Drop_ThreeSet_Venn}
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\title{Three-way Venn Diagram}
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\description{
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Plot an area-proportional three-set Venn Diagram with labels.
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}
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\usage{
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M3D_ThreeSet_Venn(set1, set2, set3, names)
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M3Drop_ThreeSet_Venn(set1, set2, set3, names)
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}
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\arguments{
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\item{set1}{a vector of items in the first set.}
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}
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\value{None}
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\examples{
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M3D_ThreeSet_Venn(c(1:20), c(15:30), c(5,10,15,20,25,30,35,40,45,50,55,60), names=c("A","B","C"))
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M3Drop_ThreeSet_Venn(c(1:20), c(15:30), c(5,10,15,20,25,30,35,40,45,50,55,60), names=c("A","B","C"))
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}
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\keyword{Venn Diagram}

‎man/M3D_getmarkers.Rd‎

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\name{M3D_getmarkers}
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\alias{M3D_getmarkers}
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\name{M3Drop_getmarkers}
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\alias{M3Drop_getmarkers}
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\title{Identify marker genes}
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\description{
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Calculates area under the ROC curve for each gene to predict the best group of cells from all other cells.
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}
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\usage{
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M3D_getmarkers(expr_mat, labels)
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M3Drop_getmarkers(expr_mat, labels)
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}
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\arguments{
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\item{expr_mat}{a numeric matrix of expression values, columns = samples, rows = genes.}
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}
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\examples{
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library(M3DExampleData)
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marker_gene_table = M3D_getmarkers(Mmus_example_list$data, Mmus_example_list$labels)
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marker_gene_table = M3Drop_getmarkers(Mmus_example_list$data, Mmus_example_list$labels)
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}
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\keyword{markers}

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