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Getting Started

Import Scimap as:

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pip install scimap
import scimap as sm

Load Data

In order to make data analysis tools interoperable, scimap has adopted the the AnnData data structure. This allows users to use a wealth of single-cell analysis tools that works with AnnData structuring.

At the most basic level, an AnnData object adata stores a data matrix adata.X, annotation of observations adata.obs and variables adata.var as pd.DataFrame and unstructured annotation adata.uns as dict. Names of observations and variables can be accessed via adata.obs_names and adata.var_names, respectively. AnnData objects can be sliced like dataframes, for example, adata_subset = adata[:, list_of_gene_names]. For more, see the AnnData page.

To initialize an AnnData object, the following can be performed.

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import anndata as ad
import pandas as pd

# Load the data
data = pd.read_csv('counts_matrix.csv') # Single-Cell counts matrix
meta = pd.read_csv('meta_data.csv') # MetaData

# Create the AnnData object
adata = ad.AnnData(data)
adata.obs = meta

Note

If you used mcmicro pipeline to process your images, scimap provides a handy function to convert mcmicro output to AnnData object.

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filepath = ['/path/to/file.csv']
adata = sm.pp.mcmicro_to_scimap (filepath)

Work Flow

The typical workflow then consists of subsequent calls of scimap tools:

Pre-Processing Tools under sm.pp.<tool>
Analysis Tools under sm.tl.<tool>
Plotting Tools under sm.pl.<tool>
Helper Tools under sm.hl.<tool>