Update README and documentation

Author: Andrew Ramirez

README.md

@@ -4,9 +4,7 @@ RISE (Reduction and Insight in Single-cell Exploration) is an unsupervised, tens
 
 RISE does not require prior cell-type labels or clustering, reducing bias and enabling discovery of novel cell states, while also separating technical, biological, and condition-driven variation without batch correction that may erase meaningful signals. Its high resolution enables the identification of cell populations and condition-specific subpopulations missed by pseudobulk or clustering-based approaches, and each resulting component is directly linked to specific conditions, genes, and cells, making the results biologically tractable.
 
-- **Discuss development** on [GitHub](https://github.com/meyer-lab/RISE).
 - **Read the documentation** at [RISE Documentation](https://meyer-lab.github.io/RISE/).
-- **Install** via `pip install git+https://github.com/meyer-lab/RISE.git@main`.
 - RISE uses the [AnnData](https://anndata.readthedocs.io/) format for handling single-cell data matrices.
 
 ## Installation

docs/index.rst

@@ -8,7 +8,9 @@ Overview
 
 RISE (Reduction and Insight in Single-cell Exploration) is an unsupervised, tensor-based computational method designed for the integrative analysis of single-cell RNA sequencing (scRNA-seq) data across multiple experimental conditions, such as drug treatments, patient cohorts, or time points. Built upon the PARAFAC2 tensor decomposition framework, 
 RISE preserves the inherent three-dimensional structure of multi-condition single-cell data—conditions x cells x genes—instead of flattening it into a conventional two-dimensional matrix. This allows RISE to decompose variation into distinct, interpretable patterns associated with experimental conditions, individual cells, and genes, providing a 
-more nuanced and biologically meaningful analysis. RISE does not require prior cell-type labels or clustering, reducing bias and enabling discovery of novel cell states, while also separating technical, biological, and condition-driven variation without  batch correction that may erase meaningful signals. 
+more nuanced and biologically meaningful analysis. 
+
+RISE does not require prior cell-type labels or clustering, reducing bias and enabling discovery of novel cell states, while also separating technical, biological, and condition-driven variation without  batch correction that may erase meaningful signals. 
 Its high resolution enables the identification of cell populations and condition-specific subpopulations missed by pseudobulk or clustering-based approaches, and each resulting component is directly linked to specific conditions, genes, and cells, making the results biologically tractable.
 
 .. toctree::

docs/references.rst

@@ -8,16 +8,10 @@ RISE Paper
 
 Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE. *Cell Systems*, 2025. DOI: `10.1016/j.cels.2025.101294 <https://doi.org/10.1016/j.cels.2025.101294>`_
 
-Factor Match Score (FMS)
-------------------------
+PARAFAC2 and Factor Match Score
+--------------------------------
 
-Factor Match Score is used to assess the stability and reproducibility of tensor decomposition results. For more information on FMS, see:
+RISE is built upon the PARAFAC2 tensor decomposition framework, which allows factor matrices in one mode to change across slices while maintaining a constant cross-product constraint. Factor Match Score (FMS) is used to assess the stability and reproducibility of tensor decomposition results across different runs.
 
-Tomasi, G., & Bro, R. (2006). A comparison of algorithms for fitting the PARAFAC model. *Computational Statistics & Data Analysis*, 50(7), 1700-1734. DOI: `10.1016/j.csda.2004.11.013 <https://doi.org/10.1016/j.csda.2004.11.013>`_
+Roald, M., Schenker, C., Calhoun, V.D., Adali, T., Bro, R., Cohen, J.E., & Acar, E. (2022). An AO-ADMM approach to constraining PARAFAC2 on all modes. *SIAM Journal on Mathematics of Data Science*, 4(3), 1191-1222. DOI: `10.1137/21M1450033 <https://doi.org/10.1137/21M1450033>`_
 
-AnnData Format
---------------
-
-RISE uses the AnnData format for storing and manipulating single-cell data:
-
-Virshup, I., Rybakov, S., Theis, F.J., Angerer, P., & Wolf, F.A. (2021). anndata: Annotated data. *bioRxiv*. DOI: `10.1101/2021.12.16.473007 <https://doi.org/10.1101/2021.12.16.473007>`_