#TAMU MATLAB HELP FREE#

Igraph::graph_from_adjacency_matrix(adjmatrix = outputH0$tensorNetworks$X, weighted = TRUE) Once installed, scTenifoldNet can be loaded typing:

p.adj: A numeric vector of adjusted p-values using Benjamini & Hochberg (1995) FDR correction.p.value: A numeric vector of the p-values associated to the fold-changes, probabilities are asigned as P using the Chi-square distribution with one degree of freedom.FC: A numeric vector of the FC computed with respect to the expectation.

Z: A numeric vector of the Z-scores computed after Box-Cox power transformation.

distance: A numeric vector of the Euclidean distance computed between the coordinates of the same gene in both conditions.

gene: A character vector with the gene id identified from the manifoldAlignment output.

It is a data frame with 6 columns as follows:

diffRegulation: The results of the differential regulation analysis.

It is a data frame with 2 times the number of genes in the rows and d (default= 30) dimensions in the columns

manifoldAlignment: The generated low-dimensional features result of the non-linear manifold alignment.

Y: The constructed network for the Y sample.

X: The constructed network for the X sample.

tensorNetworks: The computed weight-averaged denoised gene regulatory networks after CANDECOMP/PARAFAC (CP) tensor decomposition.

The output of scTenifoldNet is a list with 3 slots as follows: Below is a table of running times under different scenarios: Number of Cells Time increases proportional to the number of cells and genes in the dataset used as input. The running time of scTenifoldNet is largely dependent on how long it takes to construct scGRNs from subsampled expression matrices.

#TAMU MATLAB HELP FREE#

Given the modular structure of the package, users are free to include modifications in each step to perform their analysis. Data is expected to be not normalized if the main scTenifoldNet function is used.

The required input for scTenifoldNet is an expression matrix with genes in the rows and cells (barcodes) in the columns. Performs non-linear manifold alignment of two gene regulatory networksĮvaluates gene differential regulation based on manifold alignment distancesĬonstruct and compare single-cell gene regulatory networks (scGRNs) using single-cell RNA-seq (scRNA-seq) data sets collected from different conditions based on principal component regression, tensor decomposition, and manifold alignment. Performs CANDECOMP/PARAFAC (CP) Tensor Decomposition Performs counts per million (CPM) data normalizationĬomputes a gene regulatory network based on principal component regressionĬomputes gene regulatory networks for subsamples of cells based on principal component regression Performs single-cell data quality control Install_github('cailab-tamu/scTenifoldNet')

Tamu matlab help