Minor formating

README.md

@@ -23,13 +23,13 @@ MAGIC has a few important parameters that play important rolw to determine the q
 
 2). _ka_ : This dictates the standard deviation to be used in the Gaussian kernel. To elaborate, the standard deviation in the Gaussian kernel for a given cell is set to be the distance to it's ka-th nearest neighbor. By default, this is set to _ka_ = _k_/3 = 10.
 
-3). _t_ : This defines the power to which the Markov matrix is to be raised. This is arguably the most important parameter. A very high _t_ can lead to over-smoothed results while a low _t_ can lead to noisy results. We provide an automatic way to detect _t_ in the paper. For this, we compute the degree of change between the imputed data at time _t_ and time _t-1_ and stop after this value stabilizes. With the increase in _t_, the data goes through a rapidly changing imputation regime followed by a smoothing regime. In the imputation regime, the diffusion learns the manifold structure and removes noise. At larger values of _t_, diffusing further would smooth out real biology. The knee-point determines an optimal _t_. However, the choice of _t_ can be context-dependent. While the automatic method can act as a good guide to choose optimal _t_, the underlying mathematics may not always recapitulate the true biology. Therefore, we always recommend to spend time looking at the data and to vet the values of _t_ accordingly. For more details on the method please view run_magic.m file inside matlab directory above. 
+3). _t_ : This defines the power to which the Markov matrix is to be raised. This is arguably the most important parameter. A very high _t_ can lead to over-smoothed results while a low _t_ can lead to noisy results. We provide an automatic way to detect _t_ in the paper. For this, we compute the degree of change between the imputed data at time _t_ and time _t-1_ and stop after this value stabilizes. With the increase in _t_, the data goes through a rapidly changing imputation regime followed by a smoothing regime. In the imputation regime, the diffusion learns the manifold structure and removes noise. At larger values of _t_, diffusing further would smooth out real biology. The knee-point determines an optimal _t_. However, the choice of _t_ can be context-dependent. While the automatic method can act as a good guide to choose optimal _t_, the underlying mathematics may not always recapitulate the true biology. Therefore, we always recommend to spend time looking at the data and to vet the values of _t_ accordingly. For more details on the method please view `run_magic.m` file inside matlab directory above. 
 
-For example Python notebooks, please go inside the notebooks directory and open: Magic_single_cell_RNAseq.ipynb
+For example Python notebooks, please go inside the notebooks directory and open: `Magic_single_cell_RNAseq.ipynb`
 
-For example MATLAb usage, please go inside the notebooks directory and open: test_magic.m
+For example MATLAb usage, please go inside the notebooks directory and open: `test_magic.m`
 
-For a tutorial on how to use the GUI, please go inside docs and open: magic_tutorial.pptx
+For a tutorial on how to use the GUI, please go inside docs and open: `magic_tutorial.pptx`
 
 #### Installation and dependencies for the Python version
 1. The Python3 version of MAGIC can be installed using:
@@ -49,8 +49,8 @@ All the dependencies will be automatically installed using the above commands
 #### Usage
 
 #### Instructions for the Matlab version
-1. run_magic.m -- MAGIC imputation function
-2. test_magic.m -- Shows how to run MAGIC. Also included is a function for loading 10x format data (load_10x.m)
+1. `run_magic.m` -- MAGIC imputation function
+2. `test_magic.m` -- Shows how to run MAGIC. Also included is a function for loading 10x format data (`load_10x.m`)
 
 ##### Interactive command line
 A tutorial on MAGIC usage and results visualization for single cell RNA-seq data can be found in this notebook: https://nbviewer.jupyter.org/github/dpeerlab/magic/blob/develop/notebooks/Magic_single_cell_RNAseq.ipynb