Part II ESAI calculating

With the output of SEVtras.sEV_recognizer in Part I sEVs recognizing and cell matrix with cell type, SEVtras can track each sEV to original cell type and calculate sEV secretion activity index (ESAI).

Note1: The input cell matrix should contain sample and cell type information in the obs of adata.

Note2: This command can be compatible to all platform, including Jupyter on Windows. If you encounter a gesapy file occupation error on Windows platform, please try to rerun the same command.

SEVtras provides function ESAI_calculator to evaluate dynamic of cellular sEV secretion activity.

import SEVtras
SEVtras.ESAI_calculator(adata_ev_path='./tests/sEV_SEVtras.h5ad', adata_cell_path='./tests/test_cell.h5ad', out_path='./outputs', Xraw=False, OBSsample='batch', OBScelltype='celltype')

The first two parameters represent the path to sEV- and cell- anndata objects. sEV-anndata object comes from the output of SEVtras.sEV_recognizer. The cell-anndata object is the same as conventional single cell analysis data coming from filtered_feature_bc_matrix directory in Cell Ranger outs.

The third parameter specifies the path of the ESAI_calculator outputs. The outputs include:

  • an adata file combining both adata_ev and adata_cell, named SEVtras_combined.h5ad; ESAI_c and ESAI_cS are indexed in the obs matrix, and represent the sEV secretion activity index at the cell type level across all samples and resolved sample by sample;

  • two csv files calculating the sEV secretion activity index at the sample level (ESAI) and cell type level (ESAI_cS), named ESAI_sample.csv and ESAI_celltype.csv;

  • one pdf file embedding sEVs and cells in a umap , named SEVumap.pdf;

  • and two pdf files plotting the ESAI_c in a umap, named ESAIumap.pdf and ESAIumap_sample.pdf; Here, ESAI_c is the sEV secretion activity at the cell type level in all your samples, and ESAI_cS means the sEV secretion activity at the cell type level resolved sample by sample.

The fourth parameter means whether to use the raw object in the adata_cell or not. If adata_cell has been filtered or normalized, please set Xraw=True, and adata_cell.raw will be used (Note: save raw adata_cell as adata_cell.raw before filtering).

The last two parameters define which index represents the sample and cell type information in the obs of adata. By default, SEVtras uses the index of batch and celltype in the obs of adata_cell. We can change the index with the parameters and OBSsample and OBScelltype.

Note: The sample information in adata_ev is in the key of batch by default. If OBSsample != batch, please change the key in the adata_ev too before v0.2.12.

The original cell type for each droplet listed in the obsm of SEVtras_sEVs.h5ad indexed as source.

The result of SEVumap.pdf and ESAIumap.pdf is similar to the following:

_images/Part2.png

A higher level of ESAI indicates that this cell type has intense sEV secretion activity, which may be related to tumor tumor malignancy, invasion, metastasis and other disease progression. We recommend performing association analysis of ESAI with clinical indicators.