update readme to reformat sections

* [Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease](https://www.nature.com/articles/s41588-021-00894-z)

## scWGCNA tutorial

# scWGCNA tutorial

Here I will walk you through how to go from a processed Seurat object and then

to metacells. If you already have a clustered dataset, you can skip the next two sections down to

the "Constructing metacells" section.

### Clustering and dimensionality reduction

## Clustering and dimensionality reduction

First, I collected several datasets from published snRNA-seq studies of the human

brain in health and disease, totaling over 500k single-nucleus transcriptomes. This

![Clustered snRNA-seq dataset](./tutorial_images/umap_liger_clusters.png)

### Subset a specific cell-type of interest.

## Subset a specific cell-type of interest.

In order to identify co-expression modules within a given cell-type, we repeat the

above analysis using only a subset of the data. Here we are interested in doing this

analysis in oligodendrocyte lineage cells, including oligodendrocyte progenitors and

mature oligodendrocytes.

# ODC + OPC analysis

```{r eval=FALSE}

# subset by neuronal clusters

![ODC UMAP](./tutorial_images/umap_odc_group.png)

### Constructing ***metacells***

## Constructing ***metacells***

We are now ready to construct metacells. To save on memory, I am only using highly

variable genes to construct metacells and to perform downstream WGCNA. Your downstream

![metacell UMAP](./tutorial_images/metacell_umap_group.png)

### WGCNA

## Run WGCNA

First we format the data for WGCNA.

![ODC WGCNA Dendrogram](./tutorial_images/dendro.png)

### Visualizations

## Visualizations

Here I will show some more visualizations you can use to show the results of the

co-expression analysis:

#### Module Trajectories:

### Module Trajectories:

We plot the distribution of module eigengenes in each of the differnt ODC subgroups and

![Module Eigengenes](./tutorial_images/MEs.png)

#### Network Plots

### Network Plots

We can plot the most connected genes in each module using a network plot: