API¶
Import epiScanpy’s high-level API as:
import episcanpy.api as epi
Count Matrices: CT¶
Loading data, loading annotations, building count matrices, filtering of lowly covered methylation variables. Filtering of lowly covered cells.
Load features¶
In order to build a count matrix for either methylation or open chromatin data, loading the segmentation of the genome of interest or the set of features of interest is a prerequirement.
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The function load features is here to transform a bed file into a usable set of units to measure methylation levels. |
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Generate windows/bins of the given size for the appropriate genome (default choice is human). |
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If the features loaded are too smalls or of different sizes, it is possible to normalise them to a unique given size by extending the feature coordinate in both directions. |
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Plot the different feature sizes in an histogram. |
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Extract the names of the loaded features, specifying the chromosome they originated from. |
Reading methylation file¶
Functions to read methylation files, extract methylation and buildthe count matrices:
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Build methylation count matrix for a given annotation. |
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Read file from which you want to extract the methylation level and (assuming it is like the Ecker/Methylpy format) extract the number of methylated read and the total number of read for the cytosines covered and in the right genomic context (CG or CH) :param sample_name: name of the file to read to extract key information. |
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read the raw count matrix and convert it into an AnnData object. |
Reading open chromatin(ATAC) file¶
ATAC-seq specific functions to build count matrices and load data:
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Build a count matrix one set of features at a time. |
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Convert regular atac matrix into a sparse Anndata: |
General functions¶
Functions non -omic specific:
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Convert regular atac matrix into a sparse Anndata: |
Preprocessing: PP¶
Imputing missing data (methylation), filtering lowly covered cells or variables, correction for batch effect.
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Histogram of the number of open features (in the case of ATAC-seq data) per cell. |
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This function computes a variability score to rank the most variable features across all cells. |
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convert the count matrix into a binary matrix. |
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Automatically computes PCA coordinates, loadings and variance decomposition, a neighborhood graph of observations, t-distributed stochastic neighborhood embedding (tSNE) Uniform Manifold Approximation and Projection (UMAP) |
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Load observational metadata in adata.obs. |
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Load sparse matrix (including matrices corresponding to 10x data) as AnnData objects. |
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Filter cell outliers based on counts and numbers of genes expressed. |
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Filter features based on number of cells or counts. |
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Normalize counts per cell. |
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Principal component analysis [Pedregosa11]. |
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Normalize total counts per cell. |
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Regress out unwanted sources of variation. |
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Subsample to a fraction of the number of observations. |
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Downsample counts from count matrix. |
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Compute a neighborhood graph of observations [McInnes18]. |
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Transform adata.X from a matrix or array to a csc sparse matrix. |
Methylation matrices¶
Methylation specific count matrices.
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Impute missing values in methyaltion level matrices. |
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read the raw count matrix and convert it into an AnnData object. |
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Temporary function to load and impute methyaltion count matrix into an AnnData object |
Tools: TL¶
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It is a wrap-up function of scanpy sc.tl.rank_genes_groups function. |
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Compute silhouette scores. |
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Automatically computes PCA coordinates, loadings and variance decomposition, a neighborhood graph of observations, t-distributed stochastic neighborhood embedding (tSNE) Uniform Manifold Approximation and Projection (UMAP) |
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Convert list of known cell type markers from literature to a dictionary Input list of known marker genes First row is considered the header |
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Use markers of a given cell type to plot peak openness for peaks in promoters of the given markers Input cell type, cell type markers, peak promoter intersections |
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Diffusion Maps [Coifman05] [Haghverdi15] [Wolf18]. |
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Force-directed graph drawing [Islam11] [Jacomy14] [Chippada18]. |
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t-SNE [Maaten08] [Amir13] [Pedregosa11]. |
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Embed the neighborhood graph using UMAP [McInnes18]. |
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Cluster cells into subgroups [Blondel08] [Levine15] [Traag17]. |
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Cluster cells into subgroups [Traag18]. |
Plotting: PL¶
The plotting module episcanpy.plotting largely parallels the tl.* and a few of the pp.* functions.
For most tools and for some preprocessing functions, you’ll find a plotting function with the same name.
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Scatter plot in PCA coordinates. |
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Plot PCA results. |
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Plot the variance ratio. |
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Scatter plot in tSNE basis. |
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Scatter plot in UMAP basis. |
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Plot ranking of features. |
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Plot ranking of features for all tested comparisons. |
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Plot ranking of features using dotplot plot (see |
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Plot ranking of features using stacked_violin plot (see |
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Plot ranking of features using matrixplot plot (see |
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Plot ranking of features using heatmap plot (see |
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Plot ranking of features using heatmap plot (see |
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Violin plot. |
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Scatter plot along observations or variables axes. |
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Plot rankings. |
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Hierarchically-clustered heatmap. |
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Stacked violin plots. |
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Heatmap of the expression values of genes. |
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Makes a dot plot of the expression values of var_names. |
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Creates a heatmap of the mean expression values per cluster of each var_names If groupby is not given, the matrixplot assumes that all data belongs to a single category. |
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In this type of plot each var_name is plotted as a filled line plot where the y values correspond to the var_name values and x is each of the cells. |
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Plots a dendrogram of the categories defined in groupby. |
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Plots the correlation matrix computed as part of sc.tl.dendrogram. |
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% or cell count corresponding to the overlap of different cell types between 2 set of annotations/clusters. |
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Heatmap of the cluster correspondance between 2 set of annaotations. |
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Plot the product of tl.silhouette as a silhouette plot |
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Both compute silhouette scores and plot it. |