Fix some typos in tutorial

docs/notebooks/Tutorial.myst

@@ -9,11 +9,11 @@ kernelspec:
 # pyLemur Walkthrough
 
 
-The goal of `pyLemur` is to simplify analysis of multi-condition single-cell data. If you have collected a single-cell RNA-seq dataset with more than one condition, lemur predicts for each cell and gene how much the expression would change if the cell had been in the other condition.
+The goal of `pyLemur` is to simplify analysis of multi-condition single-cell data. If you have collected a single-cell RNA-seq dataset with more than one condition, LEMUR predicts for each cell and gene how much the expression would change if the cell had been in the other condition.
 
 `pyLemur` is a Python implementation of the LEMUR model; there is also an `R` package called [lemur](https://bioconductor.org/packages/lemur/) which provides additional functionality: identifying neighborhoods of cells that show consistent differential expression values and a pseudo-bulk test to validate the findings.
 
-`pyLemur` implements a novel framework to disentangle the effects of known covariates, latent cell states, and their interactions. At the core, is a combination of matrix factorization and regression analysis implemented as geodesic regression on Grassmann manifolds. We call this latent embedding multivariate regression. For more details see our [preprint](https://www.biorxiv.org/content/10.1101/2023.03.06.531268) {cite:p}`Ahlmann-Eltze2024`.
+`pyLemur` implements a novel framework to disentangle the effects of known covariates, latent cell states, and their interactions. At the core, is a combination of matrix factorization and regression analysis implemented as geodesic regression on Grassmann manifolds. We call this latent embedding multivariate regression (LEMUR). For more details see our [preprint](https://www.biorxiv.org/content/10.1101/2023.03.06.531268) {cite:p}`Ahlmann-Eltze2024`.
 
 <img src="../_static/images/equation_schematic.png" alt="Schematic of the matrix decomposition atthe core of LEMUR" />