Getting Started¶
Import Scimap as:
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- including scanpy.
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.
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.
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 under
sm.pp.<tool>
- analysis tools under
sm.tl.<tool>
- plotting tools under
sm.pl.<tool>
- helper tools under
sm.hl.<tool>