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DEAPLOG(Differentially Expression Analysis and Pseudotemporal Locating and Ordering of Gene by Single-cell RNA-seq data)

DEAPLOG is a tool to perform differentially expression analysis for cell clusters and other conditions, calculate the pseudotime of genes and profile genes coordinates accoding to the embedding coordinates of cells.

DEAPLOG consists of three core functions:

• (i) get_DEG_uniq to find genes that are differentially expressed in only one cell type based on normalized raw counts of scRNA-seq data;
• (ii) get_DEG_multi to find genes that are differentially expressed in one or more cell types based on normalized raw counts of scRNA-seq data;
• (iii) get_genes_location_pseudotime to calculate the pseudotemporal expression of individual genes based on pseudotime ordering of cells and to locate genes into suitable coordinates based on the embedding coordinates of cells, such as 'X_umap', 'X_diffmap' and 'X_tsne'.

Installation

PLOGS depends on numpy, scipy, pandas, scanpy,anndata. The package is available on pip and conda, and can be easily installed as follows:

pip install deaplog

Usage and Documentation

1. identifing marker genes for cell clusters: <br>

The inputs of DEAPLOG is the AnnData object of normlized counts of scRNA-seq data with pre-annotated cell clusters.

deaplog.get_DEG_uniq(rdata, adata,group_key='leiden',power=11,ratio=0.2,p_threshold=0.01,q_threshold=0.05) #find genes that are differentially expressed in only one cell type

or

deaplog.get_DEG_multi(rdata, adata,group_key='leiden',power=11,ratio=0.2,p_threshold=0.01,q_threshold=0.05) #find genes that are differentially expressed in one or more  cell types

the rdata: the Anndata of normlized counts of scRNA-seq data;<br> the adata: the Anndata of scRNA-seq data with pre-annotated cell clusters;<br> the group_key: the label for cell clusters;<br> the power: a parameter for nonlinear regression of gene expression pattern;<br> the ratio: the proportion of gene expression in cell cluter;<br> the ratio: the proportion of gene expression in cell cluter,the value is between 0 and 1;<br> the p_threshold : the threshold of p-value. the value is between 0 and 1;<br> the q_threshold : the threshold of q_value. the value is between 0 and 1;<br>

2. calculate the pseudotime of genes and profile genes map accoding to cell map:<br>

deaplog.get_genes_location_pseudotime(rdata, adata,group_key='leiden',power=11,gene_matrix= markers_s,obsm='X_umap',)

the rdata: the Anndata of normlized counts of scRNA-seq data;<br> the adata: the Anndata of scRNA-seq data with pre-annotated cell clusters;<br> the group_key: the label for cell clusters;<br> the power: a parameter for nonlinear regression of gene expression pattern;<br><br> the gene_matrix: a data.frame producted by get_DEG_uniq or get_DEG_multi. the obsm: the keys of adata.obsm.