SCMCP

Read data from various file formats (h5ad, 10x, text files, etc.) or directory path.

Write AnnData objects to file.

Filter genes based on number of cells or counts

Filter cells based on counts and numbers of genes expressed.

Calculate quality control metrics(common metrics: total counts, gene number, percentage of counts in ribosomal and mitochondrial) for AnnData.

Logarithmize the data matrix (X = log(X + 1))

Normalize counts per cell to the same total count

Principal component analysis

Annotate highly variable genes

Regress out (mostly) unwanted sources of variation.

Scale data to unit variance and zero mean

ComBat function for batch effect correction

Predict doublets using Scrublet

Compute nearest neighbors distance matrix and neighborhood graph

t-distributed stochastic neighborhood embedding (t-SNE), for visualizating single-cell data

Uniform Manifold Approximation and Projection (UMAP) for visualization

Force-directed graph drawing for visualization

Diffusion Maps for dimensionality reduction

Calculate the density of cells in an embedding

Leiden clustering algorithm for community detection

Louvain clustering algorithm for community detection

Hierarchical clustering dendrogram

Diffusion Pseudotime (DPT) analysis

Partition-based graph abstraction

Map labels and embeddings from reference data to new data

Rank genes for characterizing groups, perform differentially expressison analysis

Filter out genes based on fold change and fraction of genes

Calculate overlap between data-derived marker genes and reference markers

Score a set of genes based on their average expression

Score cell cycle genes and assign cell cycle phases

Scatter plot in PCA coordinates. default figure for PCA plot

Scatter plot for user specified embedding basis (e.g. umap, tsne, etc).

Plot violin plot of one or more variables.

Plot stacked violin plots. Makes a compact image composed of individual violin plots stacked on top of each other.

Heatmap of the expression values of genes.

Plot dot plot of expression values per gene for each group.

matrixplot, Create a heatmap of the mean expression values per group of each var_names.

tracksplot,compact plot of expression of a list of genes..

Plot a scatter plot of two variables, Scatter plot along observations or variables axes.

Plot ranking of genes(DEGs) using dotplot visualization. Defualt plot DEGs for rank_genes_groups tool

plot highly variable genes; Plot dispersions or normalized variance versus means for genes.

Plot the PCA variance ratio to visualize explained variance.

Determine if each gene meets specific conditions and store results in adata.var as boolean values.for example: mitochondrion genes startswith MT-.the tool should be call first when calculate quality control metrics for mitochondrion, ribosomal, harhemoglobin genes. or other qc_vars

list key columns in adata.var. it should be called for checking when other tools need var key column names input

List key columns in adata.obs. It should be called before other tools need obs key column names input

Check if genes exist in adata.var_names. This tool should be called before gene expression visualizations or color by genes.

merge multiple adata

List cell-cell communication method.

Get an aggregate of ligand-receptor scores from multiple Cell-cell communication methods.

Visualize cell-cell communication network using a circular plot.

Visualize cell-cell communication interactions using a dotplot.

Cell-cell communication analysis with one method (cellphonedb, cellchat,connectome, natmi, etc.)