| Title: | Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions |
|---|---|
| Description: | This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. |
| Authors: | Keegan Korthauer [cre, aut] |
| Maintainer: | Keegan Korthauer <[email protected]> |
| License: | GPL-2 |
| Version: | 1.29.0 |
| Built: | 2024-09-15 05:19:49 UTC |
| Source: | https://github.com/bioc/scDD |
Calculate empirical means and variances of selected genes in a given dataset.
calcMV(a, FC = 1, FC.thresh = NA, threshold = Inf, include.zeroes = FALSE)calcMV(a, FC = 1, FC.thresh = NA, threshold = Inf, include.zeroes = FALSE)
a |
Numeric vector of values to calculate empirical mean and variance. |
FC |
Fold change for the mean and standard deviation. Default value is 1. |
FC.thresh |
Alternate fold change for the mean and standard deviation
when the (log nonzero) mean is above the value of |
threshold |
Mean threshold value which dictates which fold change value
to use for multiplying mean and standard deviation.
Default value is Inf (so |
include.zeroes |
Logical value indicating whether the zero values should be included in the calculations of the empirical means and variances. |
Calculate empirical means and variances of selected genes in a
given dataset. Optionally, multiply
the means and standard deviations by a fold change value, which can also
vary by mean value. If the mean is
below some specified threshold threshold, use one fold change value
FC. If above the threshold, use the alternate
fold change value FC.thresh. Estimates of mean and variance are
robust to outliers.
MV Vector of two elements, first contains the empirical mean estimate, second contains the empirical variance estimate (optionally multiplied by a fold change).
Calculate parameter R and P in NB distribution
calcRP(Emean, Evar)calcRP(Emean, Evar)
Emean |
Empirical mean |
Evar |
Empirical variance |
RP Vector of two elements, first contains method of moments estimator for r and second contains method of moments estimator for p (parameters of NB distribution)
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Classify significantly DD genes into the four categories (DE, DP, DM or DB) based on posterior distributions of cluster mean parameters
classifyDD(pe_mat, condition, sig_genes, oa, c1, c2, alpha, m0, s0, a0, b0, log.nonzero = TRUE, adjust.perms = FALSE, ref, min.size = 3)classifyDD(pe_mat, condition, sig_genes, oa, c1, c2, alpha, m0, s0, a0, b0, log.nonzero = TRUE, adjust.perms = FALSE, ref, min.size = 3)
pe_mat |
Matrix with genes in rows and samples in columns. Column names indicate condition. |
condition |
Vector of condition indicators (with two possible values). |
sig_genes |
Vector of the indices of significantly DD genes
(indicating the row number of |
oa |
List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in the overall (pooled) fit. |
c1 |
List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 1 only fit |
c2 |
List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 2 only fit |
alpha |
Value for the Dirichlet concentration parameter |
m0 |
Prior mean value for generating distribution of cluster means |
s0 |
Prior precision value for generating distribution of cluster means |
a0 |
Prior shape parameter value for the generating distribution of cluster precision |
b0 |
Prior scale parameter value for the generating distribution of cluster precision |
log.nonzero |
Logical indicating whether to perform log transformation of nonzero values. |
adjust.perms |
Logical indicating whether or not to adjust the permutation tests for the sample detection rate (proportion of nonzero values). If true, the residuals of a linear model adjusted for detection rate are permuted, and new fitted values are obtained using these residuals. |
ref |
one of two possible values in condition; represents the referent category. |
min.size |
a positive integer that specifies the minimum size of a
cluster (number of cells) for it to be used
during the classification step. Any clusters containing fewer than
|
cat Character vector of the same length as sig_genes that
indicates which category of
DD each significant gene belongs to (DE, DP, DM, DB, or NC (no call))
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Function to identify additional DP genes, since clustering process can be consistent within each condition and still have differential proportion within each mode. The Bayes factor score also tends to be small when the correct number of clusters is not correctly detected; in that case differential proportion will manifest as a mean shift.
feDP(pe_mat, condition, sig_genes, oa, c1, c2, log.nonzero = TRUE, testZeroes = FALSE, adjust.perms = FALSE, min.size = 3)feDP(pe_mat, condition, sig_genes, oa, c1, c2, log.nonzero = TRUE, testZeroes = FALSE, adjust.perms = FALSE, min.size = 3)
pe_mat |
Matrix with genes in rows and samples in columns. Column names indicate condition. |
condition |
Vector of condition indicators (with two possible values). |
sig_genes |
Vector of the indices of significantly DD genes
(indicating the row number of |
oa |
List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in the overall (pooled) fit. |
c1 |
List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 1 only fit |
c2 |
List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 2 only fit |
log.nonzero |
Logical indicating whether to perform log transformation of nonzero values. |
testZeroes |
Logical indicating whether or not to test for a difference in the proportion of zeroes. This will only be done for genes that have at least one zero value (genes where all cells have a nonzero value will have a 'zero.pvalue' of NA). |
adjust.perms |
Logical indicating whether or not to adjust the permutation tests for the sample detection rate (proportion of nonzero values). If true, the residuals of a linear model adjusted for detection rate are permuted, and new fitted values are obtained using these residuals. |
min.size |
a positive integer that specifies the minimum size of a
cluster (number of cells) for it to be used
during the classification step. Any clusters containing fewer than
|
The Fisher's Exact test is used to test for independence of condition membership and clustering when the clustering is the same across conditions as it is overall (and is multimodal). When clustering within condition is not multimodal or is different across conditions (most often the case), an FDR-adjusted t-test is performed to detect overall mean shifts.
cat Character vector of the same length as sig_genes
that indicates which nonsignificant genes by
the permutation test belong to the DP category
Find the appropriate Fold Change vectors for simulation that will be use in classic differential expression case.
findFC(SCdat, index, sd.range = c(1, 3), N = 4, overExpressionProb = 0.5, plot.FC = FALSE, condition = "condition")findFC(SCdat, index, sd.range = c(1, 3), N = 4, overExpressionProb = 0.5, plot.FC = FALSE, condition = "condition")
SCdat |
An object of class |
index |
Reasonable set of genes for simulation |
sd.range |
Numeric vector of length two which describes the interval (lower, upper) of standard deviations of fold changes to randomly select. |
N |
Integer value for the number of bins to divide range of fold changes for calculating standard deviations |
overExpressionProb |
Numeric value between 0 and 1 which describes the ratio of over to under expression values to sample. |
plot.FC |
Logical indicating whether or not to plot the observed and simulated log2 fold changes. |
condition |
A character object that contains the name of the column in
|
This code is a modified version of Sam Younkin's simulate FC function. Major things that were changed are (1) standard deviations are calculated only on the nonzeroes, (2) the sampling of FCs is uniform on the log scale instead of the raw scale, and (3) the binning is done by quantiles instead of evenly spaced along the average expression values.
FC.vec Return Fold Change Vectors
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Find a reasonable set of genes (one mode and at least 25 to use for simulation.
findIndex(SCdat, condition = "condition")findIndex(SCdat, condition = "condition")
SCdat |
An object of class |
condition |
A character object that contains the name of the column in
|
Vector of indices for a reasonable set of genes that can be used for simulation.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Find the clusters that are considered outliers
findOutliers(clustering, min.size = 3)findOutliers(clustering, min.size = 3)
clustering |
Numeric vector of cluster membership (1st item (named
|
min.size |
Numeric value for the minimum number of samples a cluster must have to be considered in the robust count. Default is 3. |
Function to obtain a count of the number of clusters that is
robust to outliers. Requires at least min.size samples to
be considered
in the robust count.
The robust count of the number of unique clusters excluding those
with less than min.size samples.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Given the observations for a single gene and its clustering information, return the calculated posterior parameters
getPosteriorParams(y, mcobj, alpha, m0, s0, a0, b0)getPosteriorParams(y, mcobj, alpha, m0, s0, a0, b0)
y |
Numeric data vector for one gene (log-transformed non-zeroes) |
mcobj |
Object returned by |
alpha |
Value for the Dirichlet concentration parameter |
m0 |
Prior mean value for generating distribution of cluster means |
s0 |
Prior precision value for generating distribution of cluster means |
a0 |
Prior shape parameter value for the generating distribution of cluster precision |
b0 |
Prior scale parameter value for the generating distribution of cluster precision |
A list of posterior parameter values under the DP mixture model framework, given the data and prior parameter values.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Function to obtain the normalized joint posterior of the data and partition.
jointPosterior(y, mcobj, alpha, m0, s0, a0, b0)jointPosterior(y, mcobj, alpha, m0, s0, a0, b0)
y |
Numeric data vector for one gene (log-transformed non-zeroes) |
mcobj |
Object returned by |
alpha |
Value for the Dirichlet concentration parameter |
m0 |
Prior mean value for generating distribution of cluster means |
s0 |
Prior precision value for generating distribution of cluster means |
a0 |
Prior shape parameter value for the generating distribution of cluster precision |
b0 |
Prior scale parameter value for the generating distribution of cluster precision |
Calculates the normalized joint posterior of the data and partition under the Product Partition Model formulation of the Dirichlet Process Mixture model.
log joint posterior value
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Shortcut for length(unique())
lu(x)lu(x)
x |
Numeric vector of cluster membership (1st item (named |
Function to obtain a count of the number of clusters
The count of the number of unique clusters.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Count the number of clusters with at least min.size samples
luOutlier(x, min.size = 3)luOutlier(x, min.size = 3)
x |
Numeric vector of cluster membership (1st item (named |
min.size |
Numeric value for the minimum number of samples a cluster must have to be considered in the robust count. Default is 3. |
Function to obtain a count of the number of clusters that is robust
to outliers. Requires at least min.size samples to be considered
in the robust count.
The robust count of the number of unique clusters excluding those
with less than min.size samples.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Function to determine how many normal mixture components are present.
mclustRestricted(y, restrict = TRUE, min.size)mclustRestricted(y, restrict = TRUE, min.size)
y |
Numeric vector of values to fit to a normal mixture model with Mclust. |
restrict |
Logical indicating whether or not to enforce the restriction on cluster separation based on bimodal index and ratio of largest to smallest variance (see details). If False, then Mclust results as is are returned. |
min.size |
a positive integer that specifies the minimum size of a
cluster (number of cells) for it to be used
during the classification step. A clustering with all clusters of
size less than |
Robust to detecting multiple components that are close together by enforcing that the distance between two clusters of appreciable size (at least 4 samples), have sufficiently high bimodal index (cluster mean difference standardized by average standard deviation and multiplied by a balance factor which is one when clusters are perfectly balanced) and not have variances that differ by more than a ratio of 20. Bimodal index threshold is dependent on sample size to ensure consistent performance in power and type I error of detection of multiple components.
List object with (1) vector of cluster membership, (2) cluster means, (3) cluster variances, (4) number of model parameters, (5) sample size, (6) BIC of selected model, and (6) loglikelihood of selected model.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016- 1077-y
Function to obtain bayes factor numerator for permutations of one gene
permMclust(y, nperms, condition, remove.zeroes = TRUE, log.transf = TRUE, restrict = FALSE, alpha, m0, s0, a0, b0, ref, min.size)permMclust(y, nperms, condition, remove.zeroes = TRUE, log.transf = TRUE, restrict = FALSE, alpha, m0, s0, a0, b0, ref, min.size)
y |
Numeric data vector for one gene |
nperms |
Number of permutations of residuals to evaulate |
condition |
Vector of condition indicators for each sample |
remove.zeroes |
Logical indicating whether
zeroes need to be removed from |
log.transf |
Logical indicating whether the data is in the raw scale (if so, will be log-transformed) |
restrict |
Logical indicating whether to perform restricted Mclust clustering where close-together clusters are joined. |
alpha |
Value for the Dirichlet concentration parameter |
m0 |
Prior mean value for generating distribution of cluster means |
s0 |
Prior precision value for generating distribution of cluster means |
a0 |
Prior shape parameter value for the generating distribution of cluster precision |
b0 |
Prior scale parameter value for the generating distribution of cluster precision |
ref |
one of two possible values in condition; represents the referent category. |
min.size |
a positive integer that specifies the minimum size of a
cluster (number of cells) for it to be used
during the classification step. Any clusters containing fewer than
|
Obtains bayes factor numerator for data vector
y representing one gene
Bayes factor numerator for the current permutation
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Function to obtain bayes factor for permutations of one gene's residuals
permMclustCov(y, nperms, C, condition, remove.zeroes = TRUE, log.transf = TRUE, restrict = FALSE, alpha, m0, s0, a0, b0, ref, min.size)permMclustCov(y, nperms, C, condition, remove.zeroes = TRUE, log.transf = TRUE, restrict = FALSE, alpha, m0, s0, a0, b0, ref, min.size)
y |
Numeric data vector for one gene |
nperms |
Number of permutations of residuals to evaulate |
C |
Matrix of confounder variables, where there is one row for each sample and one column for each covariate. |
condition |
Vector of condition indicators for each sample |
remove.zeroes |
Logical indicating whether zeroes need to be removed
from |
log.transf |
Logical indicating whether the data is in the raw scale (if so, will be log-transformed) |
restrict |
Logical indicating whether to perform restricted Mclust clustering where close-together clusters are joined. |
alpha |
Value for the Dirichlet concentration parameter |
m0 |
Prior mean value for generating distribution of cluster means |
s0 |
Prior precision value for generating distribution of cluster means |
a0 |
Prior shape parameter value for the generating distribution of cluster precision |
b0 |
Prior scale parameter value for the generating distribution of cluster precision |
ref |
one of two possible values in condition; represents the referent category. |
min.size |
a positive integer that specifies the minimum size of a
cluster (number of cells) for it to be used
during the classification step. Any clusters containing fewer than
|
Obtains bayes factor numerator for data vector y
representing one gene
Bayes factor numerator for the current permutation
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Function to obtain bayes factor for all permutations of one gene (not parallelized; to be used when parallelizing over Genes)
permMclustGene(y, adjust.perms, nperms, condition, remove.zeroes = TRUE, log.transf = TRUE, restrict = TRUE, alpha, m0, s0, a0, b0, C, ref, min.size)permMclustGene(y, adjust.perms, nperms, condition, remove.zeroes = TRUE, log.transf = TRUE, restrict = TRUE, alpha, m0, s0, a0, b0, C, ref, min.size)
y |
Numeric data vector for one gene |
adjust.perms |
Logical indicating whether or not to adjust the permutation tests for the sample detection rate (proportion of nonzero values). If true, the residuals of a linear model adjusted for detection rate are permuted, and new fitted values are obtained using these residuals. |
nperms |
Number of permutations of residuals to evaulate |
condition |
A character object that contains the name of the column in
|
remove.zeroes |
Logical indicating whether zeroes need to be removed
from |
log.transf |
Logical indicating whether the data is in the raw scale (if so, will be log-transformed) |
restrict |
Logical indicating whether to perform restricted Mclust clustering where close-together clusters are joined. |
alpha |
Value for the Dirichlet concentration parameter |
m0 |
Prior mean value for generating distribution of cluster means |
s0 |
Prior precision value for generating distribution of cluster means |
a0 |
Prior shape parameter value for the generating distribution of cluster precision |
b0 |
Prior scale parameter value for the generating distribution of cluster precision |
C |
Matrix of confounder variables, where there is one row for each sample and one column for each covariate. |
ref |
one of two possible values in condition; represents the referent category. |
min.size |
a positive integer that specifies the minimum size of a
cluster (number of cells) for it to be used
during the classification step. Any clusters containing fewer than
|
Obtains bayes factor for data vector y representing one gene
Bayes factor numerator for the current permutation
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Function to generate random permutations of nonzero values.
permZero(m, size, zmat)permZero(m, size, zmat)
m |
Number of permuted sets to generate. |
size |
Number of samples present in the dataset |
zmat |
Matrix of indicators of whether the original data value is zero or not. Should contain the same number of rows and columns as original data matrix. |
Generates random permutations for all genes, where the zeroes are kept fixed (i.e. only permute the nonzero condition labels).
a list of length 'm' (nperms) where each item is a 'ngenes' by 'size' matrix
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Function to preprocess SingleCellExperiment object (1) to only keep genes with a certain number of nonzero entries, and (2) optionally apply a normalization procedure.
preprocess(SCdat, condition = "condition", zero.thresh = 0.9, scran_norm = FALSE, median_norm = FALSE)preprocess(SCdat, condition = "condition", zero.thresh = 0.9, scran_norm = FALSE, median_norm = FALSE)
SCdat |
An object of class |
condition |
A character object that contains the name of the column in
|
zero.thresh |
A numeric value between 0 and 1 that represents the maximum proportion of zeroes per gene allowable in the processed dataset |
scran_norm |
Logical indicating whether or not to normalize the data
using scran Normalization from |
median_norm |
Logical indicating whether or not to normalize the data
using Median Normalization from |
An object of class SingleCellExperiment with genes removed if
they have more than zero.thresh zeroes, and the normcounts
assay added if either scran_norm or median_norm is set to TRUE
and only counts is provided. If normcounts already exists and
either scran_norm or median_norm is set to TRUE, then the new
normalized counts are placed in the normcounts assay slot, and the
original values are moved to a new slot called normcounts-orig.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# load toy example SingleCellExperiment object data(scDatEx) # apply the preprocess function to filter out genes if they have more than # 75% zero scDatEx <- preprocess(scDatEx, zero.thresh=0.75) # apply the preprocess function again, but this time threshold on the # proportion of zeroes and apply scran normalization # set the zero.thresh argument to 0.75 so that genes with more than 75% # zeroes are filtered out # set the scran_norm argument to TRUE to return scran normalized counts scDatEx.scran <- preprocess(scDatEx, zero.thresh=0.75, scran_norm=TRUE) # set the median_norm argument to TRUE to return Median normalized counts scDatEx.median <- preprocess(scDatEx, zero.thresh=0.75, median_norm=TRUE)# load toy example SingleCellExperiment object data(scDatEx) # apply the preprocess function to filter out genes if they have more than # 75% zero scDatEx <- preprocess(scDatEx, zero.thresh=0.75) # apply the preprocess function again, but this time threshold on the # proportion of zeroes and apply scran normalization # set the zero.thresh argument to 0.75 so that genes with more than 75% # zeroes are filtered out # set the scran_norm argument to TRUE to return scran normalized counts scDatEx.scran <- preprocess(scDatEx, zero.thresh=0.75, scran_norm=TRUE) # set the median_norm argument to TRUE to return Median normalized counts scDatEx.median <- preprocess(scDatEx, zero.thresh=0.75, median_norm=TRUE)
extract results objects after running scDD analysis
results(SCdat, type = c("Genes", "Zhat.c1", "Zhat.c2", "Zhat.combined"))results(SCdat, type = c("Genes", "Zhat.c1", "Zhat.c2", "Zhat.combined"))
SCdat |
An object of class |
type |
A character variable specifying which output is desired, with possible values "Genes", "Zhat.c1", "Zhat.c2", and "Zhat.overall". The default value is "Genes", which contains a a data frame with nine columns: gene name (matches rownames of SCdat), permutation p-value for testing of independence of condition membership with clustering, Benjamini-Hochberg adjusted version of the previous column, p-value for test of difference in dropout rate (only for non-DD genes), Benjamini-Hochberg adjusted version of the previous column, name of the DD (DE, DP, DM, DB) pattern or DZ (otherwise NS = not significant), the number of clusters identified overall, the number of clusters identified in condition 1 alone, and the number of clusters identified in condition 2 alone. If |
Convenient helper function to extract the results (gene
classifications, pvalues, and clustering information). Results
data.frames/matrices are stored in the
metadata slot and can also be accessed without the help of this
convenience function by calling metadata(SCdat).
A data.frame which contains either the gene classification
and p-value results, or cluster membership information, as detailed in the
description of the type input parameter.
# load toy simulated example SingleCellExperiment object to find DD genes data(scDatExSim) # set arguments to pass to scDD function prior_param=list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01) # call the scDD function to perform permutations and classify DD genes scDatExSim <- scDD(scDatExSim, prior_param=prior_param, testZeroes=FALSE) # extract main results object RES <- results(scDatExSim)# load toy simulated example SingleCellExperiment object to find DD genes data(scDatExSim) # set arguments to pass to scDD function prior_param=list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01) # call the scDD function to perform permutations and classify DD genes scDatExSim <- scDD(scDatExSim, prior_param=prior_param, testZeroes=FALSE) # extract main results object RES <- results(scDatExSim)
Toy example data in SingleCellExperiment format for 500
genes to illustrate how to generate simulated data from example data
using simulateSet.
data(scDatEx)data(scDatEx)
An object of class SingleCellExperiment containing
data for 500 genes for 142 samples
(78 from condition 1 and 64 from condition 2). Condition labels (1 or 2)
are stored in the colData slot. The assays slot contains both normcounts
and counts for illustration, but these objects are identical.
An RData object, see Format section for details
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# load toy example data data(scDatEx)# load toy example data data(scDatEx)
Toy example data list (one item for each of two conditions) for 100 genes
to illustrate how to use the function
preprocess.
data(scDatExList)data(scDatExList)
A list of two matrices (one for each of two conditions) labeled "C1" and "C2". Each matrix contains data for 100 genes and a variable number of samples (78 in C1 and 64 in C2).
An RData object, see Format section for details
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# load toy example data list data(scDatExList)# load toy example data list data(scDatExList)
Toy example data in SingleCellExperiment format for 500
genes to illustrate how to generate simulated data from example data
using simulateSet. Contains 5 genes from each category
(DE, DP, DM, DB, EE, and EP).
data(scDatExSim)data(scDatExSim)
An object of class SingleCellExperiment containing
data for 30 genes for 200 samples
(100 from condition 1 and 100 from condition 2). Condition labels (1 or 2)
are stored in the colData slot.
Row names of the assayData slot contain the two letter category label that
the gene was simulated from (e.g. 'EE', 'DB', ...)
along with the row number (1-30).
An RData object, see Format section for details
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# load toy example of simulated data data(scDatExSim)# load toy example of simulated data data(scDatExSim)
Find genes with differential distributions (DD) across two conditions
scDD(SCdat, prior_param = list(alpha = 0.1, mu0 = 0, s0 = 0.01, a0 = 0.01, b0 = 0.01), permutations = 0, testZeroes = TRUE, adjust.perms = FALSE, param = bpparam(), parallelBy = c("Genes", "Permutations"), condition = "condition", min.size = 3, min.nonzero = NULL, level = 0.05, categorize = TRUE)scDD(SCdat, prior_param = list(alpha = 0.1, mu0 = 0, s0 = 0.01, a0 = 0.01, b0 = 0.01), permutations = 0, testZeroes = TRUE, adjust.perms = FALSE, param = bpparam(), parallelBy = c("Genes", "Permutations"), condition = "condition", min.size = 3, min.nonzero = NULL, level = 0.05, categorize = TRUE)
SCdat |
An object of class |
prior_param |
A list of prior parameter values to be used when modeling each gene as a mixture of DP normals. Default values are given that specify a vague prior distribution on the cluster-specific means and variances. |
permutations |
The number of permutations to be used in calculating
empirical p-values. If set to zero (default),
the full Bayes Factor permutation test will not be performed. Instead,
a fast procedure to identify the genes with significantly different
expression distributions will be performed using the nonparametric
Kolmogorov-Smirnov test, which tests the null hypothesis that
the samples are generated from the same continuous distribution.
This test will yield
slightly lower power than the full permutation testing framework
(this effect is more pronounced at smaller sample
sizes, and is more pronounced in the DB category), but is orders of
magnitude faster. This option
is recommended when compute resources are limited. The remaining
steps of the scDD framework will remain unchanged
(namely, categorizing the significant DD genes into patterns that
represent the major distributional changes,
as well as the ability to visualize the results with violin plots
using the |
testZeroes |
Logical indicating whether or not to test for a difference in the proportion of zeroes. This will only be done for genes that have at least one zero value (genes where all cells have a nonzero value will have a 'zero.pvalue' of NA). |
adjust.perms |
Logical indicating whether or not to adjust the permutation tests for the sample detection rate (proportion of nonzero values). If true, the residuals of a linear model adjusted for detection rate are permuted, and new fitted values are obtained using these residuals. |
param |
a |
parallelBy |
For the permutation test (if invoked), the manner in
which to parallelize. The default option
is |
condition |
A character object that contains the name of the column in
|
min.size |
a positive integer that specifies the minimum size of a
cluster (number of cells) for it to be used
during the classification step. Any clusters containing fewer than
|
min.nonzero |
a positive integer that specifies the minimum number of
nonzero cells in each condition required for the test of differential
distributions. If a gene has fewer nonzero cells per condition, it will
still be tested for DZ (if |
level |
numeric value between 0 and 1 that specifies the alpha level for significance of a differential gene test (default value 0.05). This is used to decide whether to classify a gene into one of the differential patterns. If 'testZeroes' is FALSE and the adjusted p-value for a given gene is below 'level', then the gene is categorized. Alternatively, if 'testZeroes' is TRUE, then the adjusted p-value must be below 'level/2' in order to be considered significant and categorized. This is done to control for multiple testing since 'testZeroes=TRUE' means that each gene is tested for a difference in nonzeroes and zeroes separately. |
categorize |
a logical indicating whether to determine which categories (DE, DP, DM, DB) each gene belongs to (default = TRUE). This can only be set to FALSE if 'permutations' is set to zero, since the full model fitting will automatically be carried out if permutations are run. |
Find genes with differential distributions (DD) across two conditions. Models each log-transformed gene as a Dirichlet Process Mixture of normals and uses a permutation test to determine whether condition membership is independent of sample clustering. The FDR adjusted (Benjamini-Hochberg) permutation p-value is returned along with the classification of each significant gene (with p-value less than 0.05 (or 0.025 if also testing for a difference in the proportion of zeroes)) into one of four categories (DE, DP, DM, DB). For genes that do not show significant influence, of condition on clustering, an optional test of whether the proportion of zeroes (dropout rate) is different across conditions is performed (DZ).
A SingleCellExperiment object that contains the data and
sample information from the input object, but where the results objects
are now added to the metadata slot. The metadata slot is now a
list with four items: the first (main results object) is a data.frame
with the following columns:
'gene': gene name (matches rownames of SCdat)
'DDcategory': name of the DD (DE, DP, DM, DB, DZ) pattern (or NS = not significant)
'Clusters.combined': the number of clusters identified overall
'Clusters.C1': the number of clusters identified in condition 1 alone
'Clusters.C2': the number of clusters identified in condition 2 alone
'nonzero.pvalue': permutation (or KS) p-value for testing difference in nonzero expression values
'nonzero.pvalue.adj': Benjamini-Hochberg adjusted version of the 'nonzero.pvalue'column
'zero.pvalue': p-value for test of difference in dropout rate (only if 'testZeroes' is TRUE)
'zero.pvalue': Benjamini-Hochberg adjusted version of the previous column (only if 'testZeroes' is TRUE)
‘combined.pvalue': Fisher’s combined p-value for a difference in nonzero or zero values (only if 'testZeroes' is TRUE).
'combined.pvalue.adj': Benjamini-Hochberg adjusted version of the previous column (only if 'testZeroes' is TRUE)
The remaining three elements are matrices (first for condition
1 and 2 combined,
then condition 1 alone, then condition 2 alone) that contains the cluster
memberships for each sample (cluster 1,2,3,...) in columns and
genes in rows. Zeroes, which are not involved in the clustering, are
labeled as zero. See the results function for a convenient
way to extract these results objects.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# load toy simulated example SingleCellExperiment object to find DD genes data(scDatExSim) # check that this object is a member of the SingleCellExperiment class # and that it contains 200 samples and 30 genes class(scDatExSim) show(scDatExSim) # set arguments to pass to scDD function # we will perform 100 permutations on each of the 30 genes prior_param=list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01) nperms <- 100 # call the scDD function to perform permutations, classify DD genes, # and return results # we won't perform the test for a difference in the proportion of zeroes # since none exists in this simulated toy example data # this step will take significantly longer with more genes and/or # more permutations scDatExSim <- scDD(scDatExSim, prior_param=prior_param, permutations=nperms, testZeroes=FALSE)# load toy simulated example SingleCellExperiment object to find DD genes data(scDatExSim) # check that this object is a member of the SingleCellExperiment class # and that it contains 200 samples and 30 genes class(scDatExSim) show(scDatExSim) # set arguments to pass to scDD function # we will perform 100 permutations on each of the 30 genes prior_param=list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01) nperms <- 100 # call the scDD function to perform permutations, classify DD genes, # and return results # we won't perform the test for a difference in the proportion of zeroes # since none exists in this simulated toy example data # this step will take significantly longer with more genes and/or # more permutations scDatExSim <- scDD(scDatExSim, prior_param=prior_param, permutations=nperms, testZeroes=FALSE)
Plots two histograms side by side with smoothed density overlay
sideHist(x, y, logT = TRUE, title.gene = "")sideHist(x, y, logT = TRUE, title.gene = "")
x |
First numeric vector of data to plot. |
y |
Second numeric vector of data to plot. |
logT |
Logical that indicates whether to take the log(x+1) transformation. |
title.gene |
Character vector that contains the gene name that you are plotting |
NULL (creates a baseR plot)
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Plots two histograms side by side with smoothed density overlay
sideViolin(y, cond, MAP = NULL, logT = TRUE, title.gene = "", conditionLabels = unique(cond), axes.titles = TRUE)sideViolin(y, cond, MAP = NULL, logT = TRUE, title.gene = "", conditionLabels = unique(cond), axes.titles = TRUE)
y |
Numeric vector of data to plot. |
cond |
Vector of condition labels corresponding to elements of |
MAP |
List of MAP partition estimates with conditions as list items and samples as elements (integer indicating which cluster each observation belongs to; zeroes belong to cluster 1) |
logT |
Logical that indicates whether to take the log(x+1) transformation. |
title.gene |
Character vector that contains the gene name that you are plotting. |
conditionLabels |
Character vector containing the names of the two conditions. |
axes.titles |
Logical indicating whether or not to include axes labels on plots. |
ggplot object
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# load toy simulated example ExpressionSet to find DD genes data(scDatExSim) # load SingleCellExperiment package to facilitate subset operations library(SingleCellExperiment) # plot side by side violin plots for Gene 1 (DE) sideViolin(normcounts(scDatExSim)[1,], scDatExSim$condition, title.gene=rownames(scDatExSim)[1]) # plot side by side violin plots for Gene 6 (DP) sideViolin(normcounts(scDatExSim)[6,], scDatExSim$condition, title.gene=rownames(scDatExSim)[6]) # plot side by side violin plots for Gene 11 (DM) sideViolin(normcounts(scDatExSim)[11,], scDatExSim$condition, title.gene=rownames(scDatExSim)[11]) # plot side by side violin plots for Gene 16 (DB) sideViolin(normcounts(scDatExSim)[16,], scDatExSim$condition, title.gene=rownames(scDatExSim)[16]) # plot side by side violin plots for Gene 21 (EP) sideViolin(normcounts(scDatExSim)[21,], scDatExSim$condition, title.gene=rownames(scDatExSim)[21]) # plot side by side violin plots for Gene 26 (EE) sideViolin(normcounts(scDatExSim)[26,], scDatExSim$condition, title.gene=rownames(scDatExSim)[26])# load toy simulated example ExpressionSet to find DD genes data(scDatExSim) # load SingleCellExperiment package to facilitate subset operations library(SingleCellExperiment) # plot side by side violin plots for Gene 1 (DE) sideViolin(normcounts(scDatExSim)[1,], scDatExSim$condition, title.gene=rownames(scDatExSim)[1]) # plot side by side violin plots for Gene 6 (DP) sideViolin(normcounts(scDatExSim)[6,], scDatExSim$condition, title.gene=rownames(scDatExSim)[6]) # plot side by side violin plots for Gene 11 (DM) sideViolin(normcounts(scDatExSim)[11,], scDatExSim$condition, title.gene=rownames(scDatExSim)[11]) # plot side by side violin plots for Gene 16 (DB) sideViolin(normcounts(scDatExSim)[16,], scDatExSim$condition, title.gene=rownames(scDatExSim)[16]) # plot side by side violin plots for Gene 21 (EP) sideViolin(normcounts(scDatExSim)[21,], scDatExSim$condition, title.gene=rownames(scDatExSim)[21]) # plot side by side violin plots for Gene 26 (EE) sideViolin(normcounts(scDatExSim)[26,], scDatExSim$condition, title.gene=rownames(scDatExSim)[26])
Simulation for Differential "Both" Case - both Differential Modality and Differential Mean
simuDB(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, DP, generateZero, constantZero, varInflation)simuDB(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, DP, generateZero, constantZero, varInflation)
Dataset1 |
Numeric matrix of expression values with genes in rows and samples in columns. |
Simulated_Data |
Required input empty matrix to provide structure information of output matrix with simulated data |
DEIndex |
Index for DE genes |
samplename |
The name for genes that chosen for simulation |
Zeropercent_Base |
Zero percentage for corresponding gene expression values |
f |
Fold change values (number of SDs) for each gene |
FC |
Fold Change values for DE Simulation |
coeff |
Relationship coefficients for Mean and Variance |
RP |
matrix for NB parameters for genes in samplename |
modeFC |
Vector of values to use for fold changes between modes for DP, DM, and DB. |
DP |
Differetial Proportion vector |
generateZero |
Specification of how to generate the zero values.
If " |
constantZero |
Numeric value between 0 and 1 that indicates the fixed
proportion of zeroes for every gene.
Ignored if |
varInflation |
Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor. |
Simulated_Data Simulated dataset for DB
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Simulation for Classic Differentially Expressed Case.
simuDE(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, generateZero, constantZero, varInflation)simuDE(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, generateZero, constantZero, varInflation)
Dataset1 |
Numeric matrix of expression values with genes in rows and samples in columns. |
Simulated_Data |
Required input empty matrix to provide structure information of output matrix with simulated data |
DEIndex |
Index for DE genes |
samplename |
The name for genes that chosen for simulation |
Zeropercent_Base |
Zero percentage for corresponding gene expression values |
f |
Fold change values (number of SDs) for each gene |
FC |
Fold Change values for DE Simulation |
coeff |
Relationship coefficients for Mean and Variance |
RP |
matrix for NB parameters for genes in samplename |
modeFC |
Vector of values to use for fold changes between modes for DP, DM, and DB. |
generateZero |
Specification of how to generate the zero values.
If " |
constantZero |
Numeric value between 0 and 1 that indicates the fixed
proportion of zeroes for every gene.
Ignored if |
varInflation |
Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor. |
Method called by main function singleCellSimu to
simulate genes
that have different means in each condition. Not intended to be called
directly by user.
Simulated_Data Simulated dataset for DE
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Simulation for Differential Modalities Case
simuDM(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, generateZero, constantZero, varInflation)simuDM(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, generateZero, constantZero, varInflation)
Dataset1 |
Numeric matrix of expression values with genes in rows and samples in columns. |
Simulated_Data |
Required input empty matrix to provide structure information of output matrix with simulated data |
DEIndex |
Index for DE genes |
samplename |
The name for genes that chosen for simulation |
Zeropercent_Base |
Zero percentage for corresponding gene expression values |
f |
Fold change values (number of SDs) for each gene |
FC |
Fold Change values for DE Simulation |
coeff |
Relationship coefficients for Mean and Variance |
RP |
matrix for NB parameters for genes in samplename |
modeFC |
Vector of values to use for fold changes between modes for DP, DM, and DB. |
generateZero |
Specification of how to generate the zero values.
If " |
constantZero |
Numeric value between 0 and 1 that indicates the fixed
proportion of zeroes for every gene.
Ignored if |
varInflation |
Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor. |
Simulated_Data Simulated dataset for DM
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Simulation for Differential Proportion Case
simuDP(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, DP, generateZero, constantZero, varInflation)simuDP(Dataset1, Simulated_Data, DEIndex, samplename, Zeropercent_Base, f, FC, coeff, RP, modeFC, DP, generateZero, constantZero, varInflation)
Dataset1 |
Numeric matrix of expression values with genes in rows and samples in columns. |
Simulated_Data |
Required input empty matrix to provide structure information of output matrix with simulated data |
DEIndex |
Index for DE genes |
samplename |
The name for genes that chosen for simulation |
Zeropercent_Base |
Zero percentage for corresponding gene expression values |
f |
Fold change values (number of SDs) for each gene |
FC |
Fold Change values for DE Simulation |
coeff |
Relationship coefficients for Mean and Variance |
RP |
matrix for NB parameters for genes in samplename |
modeFC |
Vector of values to use for fold changes between modes for DP, DM, and DB. |
DP |
Differetial Proportion vector |
generateZero |
Specification of how to generate the zero values.
If " |
constantZero |
Numeric value between 0 and 1 that indicates the fixed
proportion of zeroes for every gene.
Ignored if |
varInflation |
Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor. |
Simulated_Data Simulated dataset for DP
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
Simulation of a complete dataset, where the number of each type of differential distributions and equivalent distributions is specified.
simulateSet(SCdat, numSamples = 100, nDE = 250, nDP = 250, nDM = 250, nDB = 250, nEE = 5000, nEP = 4000, sd.range = c(1, 3), modeFC = c(2, 3, 4), plots = TRUE, plot.file = NULL, random.seed = 284, varInflation = NULL, condition = "condition", param = bpparam())simulateSet(SCdat, numSamples = 100, nDE = 250, nDP = 250, nDM = 250, nDB = 250, nEE = 5000, nEP = 4000, sd.range = c(1, 3), modeFC = c(2, 3, 4), plots = TRUE, plot.file = NULL, random.seed = 284, varInflation = NULL, condition = "condition", param = bpparam())
SCdat |
An object of class |
numSamples |
numeric value for the number of samples in each condition to simulate |
nDE |
Number of DE genes to simulate |
nDP |
Number of DP genes to simulate |
nDM |
Number of DM genes to simulate |
nDB |
Number of DB genes to simulate |
nEE |
Number of EE genes to simulate |
nEP |
Number of EP genes to simulate |
sd.range |
Numeric vector of length two which describes the interval (lower, upper) of standard deviations of fold changes to randomly select. |
modeFC |
Vector of values to use for fold changes between modes for DP, DM, and DB. |
plots |
Logical indicating whether or not to generate fold change and validation plots |
plot.file |
Character containing the file string if the plots are to be sent to a pdf instead of to the standard output. |
random.seed |
Numeric value for a call to |
varInflation |
Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor. |
condition |
A character object that contains the name of the column in
|
param |
a |
An object of class SingleCellExperiment that contains
simulated single-cell expression and metadata. The assays
slot contains a named list of matrices, where the simulated counts are
housed in the one named normcounts. This matrix should have one
row for each gene (nDE + nDP + nDM + nDB + nEE
+ nEP rows) and one sample for each column (numSamples columns).
The colData slot contains a data.frame with one row per
sample and a column that represents biological condition, which is
in the form of numeric values (either 1 or 2) that indicates which
condition each sample belongs to (in the same order as the columns of
normcounts). The rowData slot contains information about the
category of the gene (EE, EP, DE, DM, DP, or DB), as well as the simulated
foldchange value.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# Load toy example ExpressionSet to simulate from data(scDatEx) # check that this object is a member of the ExpressionSet class # and that it contains 142 samples and 500 genes class(scDatEx) show(scDatEx) # set arguments to pass to simulateSet function # we will simuate 30 genes total; 5 genes of each type; # and 100 samples in each of two conditions nDE <- 5 nDP <- 5 nDM <- 5 nDB <- 5 nEE <- 5 nEP <- 5 numSamples <- 100 seed <- 816 # create simulated set with specified numbers of DE, DP, DM, DM, EE, and # EP genes, # specified number of samples, DE genes are 2 standard deviations apart, and # multimodal genes have modal distance of 4 standard deviations SD <- simulateSet(scDatEx, numSamples=numSamples, nDE=nDE, nDP=nDP, nDM=nDM, nDB=nDB, nEE=nEE, nEP=nEP, sd.range=c(2,2), modeFC=4, plots=FALSE, random.seed=seed)# Load toy example ExpressionSet to simulate from data(scDatEx) # check that this object is a member of the ExpressionSet class # and that it contains 142 samples and 500 genes class(scDatEx) show(scDatEx) # set arguments to pass to simulateSet function # we will simuate 30 genes total; 5 genes of each type; # and 100 samples in each of two conditions nDE <- 5 nDP <- 5 nDM <- 5 nDB <- 5 nEE <- 5 nEP <- 5 numSamples <- 100 seed <- 816 # create simulated set with specified numbers of DE, DP, DM, DM, EE, and # EP genes, # specified number of samples, DE genes are 2 standard deviations apart, and # multimodal genes have modal distance of 4 standard deviations SD <- simulateSet(scDatEx, numSamples=numSamples, nDE=nDE, nDP=nDP, nDM=nDM, nDB=nDB, nEE=nEE, nEP=nEP, sd.range=c(2,2), modeFC=4, plots=FALSE, random.seed=seed)
Called by simulateSet to simulate a specified number of genes
from
one DD category at a time.
singleCellSimu(Dataset1, Method, index, FC, modeFC, DP, Validation = FALSE, numGenes = 1000, numDE = 100, numSamples = 100, generateZero = c("empirical", "simulated", "constant"), constantZero = NULL, varInflation = NULL)singleCellSimu(Dataset1, Method, index, FC, modeFC, DP, Validation = FALSE, numGenes = 1000, numDE = 100, numSamples = 100, generateZero = c("empirical", "simulated", "constant"), constantZero = NULL, varInflation = NULL)
Dataset1 |
Numeric matrix of expression values with genes in rows and samples in columns. |
Method |
Type of simulation should choose from "DE" "DP" "DM" "DB" |
index |
Reasonable set of genes for simulation |
FC |
Fold Change values for DE Simulation |
modeFC |
Vector of values to use for fold changes between modes for DP, DM, and DB. |
DP |
Differetial Proportion vector |
Validation |
Show Validation plots or not |
numGenes |
numeric value for the number of genes to simulate |
numDE |
numeric value for the number of genes that will differ between two conditions |
numSamples |
numeric value for the number of samples in each condition to simulate |
generateZero |
Specification of how to generate the zero values.
If " |
constantZero |
Numeric value between 0 and 1 that indicates the fixed
proportion of zeroes for every gene.
Ignored if |
varInflation |
Optional numeric vector with one element for each condition that corresponds to the multiplicative variance inflation factor to use when simulating data. Useful for sensitivity studies to assess the impact of confounding effects on differential variance across conditions. Currently assumes all samples within a condition are subject to the same variance inflation factor. |
Simulated_Data A list object where the first element contains a matrix of the simulated dataset, the second element contains the DEIndex, and the third element contains the fold change (between two conditions for DE, between two modes for DP, DM, and DB).
Function to perform KS test
testKS(dat, condition, inclZero = TRUE, numDE = NULL, DEIndex)testKS(dat, condition, inclZero = TRUE, numDE = NULL, DEIndex)
dat |
Matrix of single-cell RNA-seq data with genes in rows and samples in columns. |
condition |
Vector containing the indicator of which condition each
sample
(in the columns of |
inclZero |
Logical indicating whether to include zero in the test of different distributions |
numDE |
numeric value for the number of genes that will differ between two conditions |
DEIndex |
Vector containing the row numbers of the DE genes |
List object containing the significant gene indices, their adjusted p-values, and (if DE genes are supplied) the power and fdr.
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y
# load toy simulated example ExpressionSet to find KS genes data(scDatExSim) # load SingleCellExperiment package to facilitate subset operations library(SingleCellExperiment) # check that this object is a member of the ExpressionSet class # and that it contains 200 samples and 30 genes class(scDatExSim) show(scDatExSim) # perform KS test and obtain adjusted p-values RES_KS <- testKS(normcounts(scDatExSim), scDatExSim$condition, inclZero=FALSE, numDE=20, DEIndex=1:20)# load toy simulated example ExpressionSet to find KS genes data(scDatExSim) # load SingleCellExperiment package to facilitate subset operations library(SingleCellExperiment) # check that this object is a member of the ExpressionSet class # and that it contains 200 samples and 30 genes class(scDatExSim) show(scDatExSim) # perform KS test and obtain adjusted p-values RES_KS <- testKS(normcounts(scDatExSim), scDatExSim$condition, inclZero=FALSE, numDE=20, DEIndex=1:20)
Test for a difference in the proportion of zeroes between conditions for a specified set of genes
testZeroes(dat, cond, these = 1:nrow(dat))testZeroes(dat, cond, these = 1:nrow(dat))
dat |
Matrix of single cell expression data with genes in rows and samples in columns. |
cond |
Vector of condition labels |
these |
vector of row numbers (gene numbers) to test for a difference in the proportion of zeroes. |
Test for a difference in the proportion of zeroes between conditions that is not explained by the detection rate. Utilizes Bayesian logistic regression.
Vector of FDR adjusted p-values
Draw validation plots to show that the simulated dataset emulates characteristics of observed dataset.
validation(MV, DEIndex, Zeropercent_Base, Simulated_Data, numGenes)validation(MV, DEIndex, Zeropercent_Base, Simulated_Data, numGenes)
MV |
Mean and Variance matrix for observed data |
DEIndex |
Index for genes chosen to be DE (can be NULL) |
Zeropercent_Base |
Zero percentage for corresponding gene expression values |
Simulated_Data |
Simulated dataset |
numGenes |
numeric value for the number of genes to simulate |
Validation plots
Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y