Update README.rst

README.rst

@@ -42,8 +42,8 @@ SuSiE-PCA
 SuSiE PCA is the abbreviation for the Sum of Single Effects model [1]_ for principal component analysis. We develop SuSiE PCA
 for an efficient variable selection in PCA when dealing with high dimensional data with sparsity, and for quantifying
 uncertainty of contributing features for each latent component through posterior inclusion probabilities (PIPs). We
-implement the model with the `JAX <https://github.com/google/jax>`_ library developed by Google which enable the fast
-training on CPU, GPU or TPU.
+implement the model with the `JAX <https://github.com/google/jax>`_ library developed by Google which enables fast
+training on CPU, GPU, or TPU. The paper has been published in iScience: https://www.sciencedirect.com/science/article/pii/S2589004223022587
 
 |Documentation|_ | |Installation|_ | |Example|_ | |Notes|_ | |References|_ | |Support|_
 
@@ -66,9 +66,9 @@ $$w_{kl} \\sim \\mathcal{N}(0,\\sigma^2_{0kl})$$
 $$\\gamma_{kl} | \\pi \\sim \\text{Multi}(1,\\pi) $$
 
 Notice that each row vector $\\mathbf{w}_k$ is a sum of single effect vector $\\mathbf{w}_{kl}$, which is length $P$ vector
-contains only one non-zero effect $w_{kl}$ and zero elsewhere. And the coordinate of the non-zero effect is determined by
+contains only one non-zero effect $w_{kl}$ and zero elsewhere. The coordinate of the non-zero effect is determined by
 $\\gamma_{kl}$ that follows a multinomial distribution with parameter $\\pi$. By construction, each factor inferred from the
-SuSiE PCA will have at most $L$ number of associated features from the original data. Moreover, we can quantify the probability
+SuSiE PCA will have at most $L$ associated features from the original data. Moreover, we can quantify the probability
 of the strength of association through the posterior inclusion probabilities (PIPs). Suppose the posterior distribution of
 $\\gamma_{kl} \\sim \\text{Multi}(1,\\mathbf{\\alpha}_{kl})$, then the probability the feature $i$ contributing to the factor
 $\\mathbf{w}_k$ is given by:
@@ -82,7 +82,7 @@ Install SuSiE PCA
 =================
 The source code for SuSiE PCA is written fully in Python 3.8 with JAX (see
 `JAX installation guide <https://github.com/google/jax#installation>`_ for JAX). Follow the code provided below to quickly
-get started using SuSiE PCA. Users can clone this github repository and install the SuSiE PCA. (Pypi installation will
+get started using SuSiE PCA. Users can clone this GitHub repository and install the SuSiE PCA. (Pypi installation will
 be supported soon).
 
 .. code:: bash