Welcome to Scimap! 🚀
Begin your journey into the fascinating world of spatial single-cell analysis with Scimap, a comprehensive toolkit designed to empower your single-cell data exploration.
Installation 📦
Kick off by installing Scimap through these simple commands:
We highly advise creating a separate environment for installing Scimap to ensure a smooth and conflict-free setup. For comprehensive guidance on this process, please refer to our tutorials.
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This setup provides you with the foundational tools needed for your single-cell analysis endeavors.
Data Loading and Integration 🔄
Scimap champions the interoperability of single-cell analysis tools by embracing the AnnData
data structure. This strategic choice allows seamless use of numerous single-cell analysis utilities alongside AnnData
.
The AnnData Framework 🧬
An AnnData
object, adata
, encapsulates a data matrix adata.X
, with annotations of observations adata.obs
and variables adata.var
as pd.DataFrame
, and unstructured annotation adata.uns
as a dictionary. Observation and variable names are accessible via adata.obs_names
and adata.var_names
, respectively. AnnData objects support slicing, similar to dataframes: adata_subset = adata[:, list_of_gene_names]
. Explore AnnData
further in the official documentation.
Initializing AnnData Objects 🔄
To begin with an AnnData object, proceed as follows:
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!!! Note 📝
Leveraging the mcmicro pipeline? Scimap simplifies converting mcmicro
outputs into an AnnData
object:
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Navigating Non-mcmicro Data 🧐
What to do if your dataset wasn't processed with mcmicro? Ensuring your data aligns with Scimap's expectations is vital for smooth analysis:
- Spatial Assumptions: Spatial functions expect XY coordinates in 'X_centroid' and 'Y_centroid' columns. If your data differs, specify your columns when using these functions.
- Manual Data Integration: Here's how to manually prepare your data for Scimap analysis:
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Key Steps for Data Preparation 🗝️
- Unique Image Identification: Include a
imageid
column in your metadata for easy data retrieval. - Preserving Raw Data: Store unprocessed data in
adata.raw
for reference. - Log Transformation Layer: Create a
log
layer for log-transformed data normalization. - Marker Annotation: Keep a record of image markers in
adata.uns['all_markers']
for clarity during analysis.
Saving Your AnnData Object 💾
An AnnData object centralizes your data and analysis, making it simple to share and collaborate. To save your work:
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This streamlined approach facilitates comprehensive analyses, enabling you to leverage Scimap's full suite of tools and integrate with other analysis frameworks seamlessly.
Your Workflow Journey 🛤️
With Scimap, navigate through a suite of tools designed to enrich your analysis:
- Pre-Processing Tools:
sm.pp.<tool>
for data preparation. - Analysis Tools:
sm.tp.<tool>
for in-depth insights. - Plotting Tools:
sm.pl.<tool>
for impactful visualizations. - Helper Tools:
sm.hl.<tool>
for additional functionalities.
Embark on your spatial single-cell analysis journey with Scimap today and unlock the potential within your data. 🌟