This function determines k nearest neighbours for each cell in gene expression space, and tests if the links are supported by a negative binomial joint distribution of gene expression. A probability is assigned to each link which is given by the minimum joint probability across all genes.
pruneKnn(
expData,
distM = NULL,
large = TRUE,
regNB = TRUE,
bmethod = NULL,
batch = NULL,
regVar = NULL,
offsetModel = TRUE,
thetaML = FALSE,
theta = 10,
ngenes = 2000,
span = 0.75,
pcaComp = NULL,
tol = 1e-05,
algorithm = "kd_tree",
metric = "pearson",
genes = NULL,
knn = 25,
do.prune = TRUE,
alpha = 1,
nb = 3,
no_cores = NULL,
FSelect = FALSE,
pca.scale = FALSE,
ps = 1,
seed = 12345,
theta.harmony = NULL,
...
)List object of six components:
Distance matrix.
PCA transformation of expData including the first pcaComp principle components, computed on including genes or variable genes only if Fselect equals TRUE. Is is set to NULL if large equals FALSE.
Matrix of link probabilities between a cell and each of its k nearest neighbours (Bonferroni-corrected p-values). Column i shows the k nearest neighbour link probabilities for cell i in matrix x.
Matrix of uncorrected link probabilities between a cell and each of its k nearest neighbours (without multiple-testing correction). Column i shows the k nearest neighbour link probabilities for cell i in matrix x.
Matrix of column indices of k nearest neighbours for each cell according to input matrix x. First entry corresponds to index of the cell itself. Columns contain the k nearest neighbour indices for cell i in matrix x.
List object with background model of gene expression as obtained by fitBackVar function.
If regNB=TRUE this argument contains a list of four components: component pearsonRes contains a matrix of the Pearson Residual computed from the negative binomial regression, component nbRegr contains a matrix with the regression coefficients, component nbRegrSmooth contains a matrix with the smoothed regression coefficients, and log_umi is a vector with the total log UMI count for each cell. The regression coefficients comprise the dispersion parameter theta, the intercept, the regression coefficient beta for the log UMI count, and the regression coefficients of the batches (if batch is not NULL).
Vector of inferred values for the alpha parameter for each neighbourhood (if input parameter alpha is NULL; otherwise all values are equal to the input parameter).
List object storing the run parameters.
Principal component analysis of the of the input data, if large is TRUE. Output or the function irlba from the irlba package with pcaComp principal components, or 100 principal components if pcaComp is NULL.
Matrix of gene expression values with genes as rows and cells as columns. These values have to correspond to unique molecular identifier counts. Alternatively, a Seurat object could be used as input, after normalization, PCA-dimensional reduction, and shared-nearest neighbour inference.
Optional distance matrix used for determining k nearest neighbours. Default is NULL and the distance matrix is computed using a metric given by the parameter metric.
logical. If TRUE then no distance matrix is required and nearest neighbours are inferred by the FNN package based on a reduced
feature matrix computed by a principle component analysis. Only the first pcaComp principle components are considered. Prior to principal component
analysis a negative binomial regression is performed to eliminate the dependence on the total number of transcripts per cell. The pearson residuals of
this regression serve as input for the principal component analysis after smoothing the parameter dependence on the mean by a loess regression.
Deafult is TRUE. Recommended mode for very large datasets, where storing a distance matrix requires too much memory. distM
will be ignored if large is TRUE.
logical. If TRUE then gene a negative binomial regression is performed to prior to the principle component analysis if large = TRUE. See large. Otherwise, transcript counts in each cell are normalized to one, multipled by the minimal total transcript count across all cells, followed by adding a pseudocount of 0.1 and taking the logarithm. Default is TRUE.
Character string indicating the batch correction method. If "harmony", then batch correction is performed by the harmony package. Default is NULL and batch correction will be done by negative binomial regression.
vector of batch variables. Component names need to correspond to valid cell IDs, i.e. column names of expData. If regNB is TRUE, than the batch variable will be regressed out simultaneously with the log UMI count per cell. An interaction term is included for the log UMI count with the batch variable. Default value is NULL.
data.frame with additional variables to be regressed out simultaneously with the log UMI count and the batch variable (if batch is TRUE). Column names indicate variable names (name beta is reserved for the coefficient of the log UMI count), and rownames need to correspond to valid cell IDs, i.e. column names of expData. Interaction terms are included for each variable in regVar with the batch variable (if batch is TRUE). Default value is NULL.
Logical parameter. Only considered if regNB is TRUE. If TRUE then the beta (log UMI count) coefficient is set to 1 and the intercept is computed analytically as the log ration of UMI counts for a gene and the total UMI count across all cells. Batch variables and additional variables in regVar are regressed out with an offset term given by the sum of the intercept and the log UMI count. Default is TRUE.
Logical parameter. Only considered if offsetModel equals TRUE. If TRUE then the dispersion parameter is estimated by a maximum likelihood fit. Otherwise, it is set to theta. Default is FALSE.
Positive real number. Fixed value of the dispersion parameter. Only considered if theaML equals FALSE.
Positive integer number. Randomly sampled number of genes (from rownames of expData) used for predicting regression coefficients (if regNB=TRUE). Smoothed coefficients are derived for all genes. Default is 2000.
Positive real number. Parameter for loess-regression (see large) controlling the degree of smoothing. Default is 0.75.
Positive integer number. Number of princple components to be included if large is TRUE. Default is NULL and the number of principal components used for dimensionality reduction of the feature matrix is derived by an elbow criterion. However, the minimum number of components will be set to 15 if the elbow criterion results in a smaller number. The derived number can be be plotted using the plotPC function.
Numerical value greater than zero. Tolerance for numerical PCA using irlba. Default value is 1e-6.
Algorithm for fast k nearest neighbour inference, using the get.knn function from the FNN package.
See help(get.knn). Deafult is "kd_tree".
Distances are computed from the expression matrix x after optionally including only genes given as argument genes or after optional feature selection (see FSelect).
Possible values for metric are "pearson", "spearman", "logpearson", "euclidean". Default is "pearson". In case of the correlation based methods,
the distance is computed as 1 – correlation. This parameter is only used if large is FALSE and distM is NULL.
Vector of gene names corresponding to a subset of rownames of x. Only these genes are used for the computation of a distance matrix and for the computation of joint probabilities of nearest neighbours. Default is NULL and all genes are used.
Positive integer number. Number of nearest neighbours considered for each cell. Default is 25.
Logical parameter. If TRUE, then pruning of k-nearest neighbourhoods is performed. If FALSE, then no pruning is done. Default is TRUE.
Positive real number. Relative weight of a cell versus its k nearest neigbour applied for the derivation of joint probabilities. A cell receives a weight of alpha while the weights of its k nearest neighbours as determined by quadratic programming sum up to one. The sum across all weights and alpha is normalized to one, and the weighted mean expression is used for computing the link porbabilities for each of the k nearest neighbours. Larger values give more weight to the gene expression observed in a cell versus its neighbourhood. Typical values should be in the range of 0 to 10. Default is value is 1. If alpha is set to NULL it is inferred by an optimization, i.e., alpha is minimized under the constraint that the gene expression in a cell does not deviate more then one standard deviation from the predicted weigthed mean, where the standard deviation is calculated from the predicted mean using the background model (the average dependence of the variance on the mean expression). This procedure is coputationally more intense and inceases the run time of the function significantly.
Positive integer number. Number of genes with the lowest outlier probability included for calculating the link probabilities for the knn pruning. The link probability is computed as the geometric mean across these genes. Default is 3.
Positive integer number. Number of cores for multithreading. If set to NULL then the number of available cores minus two is used. Default is NULL.
Logical parameter. If TRUE, then feature selection is performed prior to distance matrix calculation and VarID analysis. Default is FALSE.
Logical parameter. If TRUE, then input features are scaled prior to PCA transformation. Default is FALSE.
Real number greater or equal to zero. Pseudocount to be added to counts within local neighbourhoods for outlier identification and pruning. Default is 1.
Integer number. Random number to initialize stochastic routines. Default is 12345.
theta parameter of RunHarmony function from the harmony package (to avoid collision with the dispersion parameter theta). Default is NULL.
Additional parameters for RunHarmony function from the harmony package, if batch is not NULL and bmethod="harmony".
res <- pruneKnn(intestinalDataSmall,knn=10,alpha=1,no_cores=1,FSelect=FALSE)
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