### Load scDist Package

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Loads the scDist package in R. Ensure the package is installed before running this command.

```r
library(scDist)
set.seed(1126490984)
```

--------------------------------

### Display Simulated Metadata

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Shows the first few rows of the metadata associated with the simulated data. The metadata should include columns for response, patient, and clusters.

```r
rownames(sim$Y) <- 1:1000
head(sim$meta.data)
```

--------------------------------

### Inspect Simulated Data Dimensions

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Prints the dimensions of the normalized counts matrix (sim$Y) from the simulated data. This helps verify the structure of the input data.

```r
dim(sim$Y) #Normalized counts
```

--------------------------------

### View scDist Results Summary

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Displays the results data frame from the scDist output. This summary includes estimated distances, confidence intervals, and p-values for each cell type.

```r
out$results
```

--------------------------------

### Display True Distances

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Shows the true distances used in the simulation for comparison with the scDist results. The true distances are stored in sim$D.true.

```r
names(sim$D.true) <- letters[1:length(sim$D.true)]
sim$D.true
```

--------------------------------

### Simulate Single-Cell Data

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Generates simulated single-cell RNA-seq data with specified number of cell types and patients per group. This function is used to create sample data for testing scDist.

```r
sim <- simData(nct=10,N1=5,N2=5)
```

--------------------------------

### Plot scDist Results

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Generates a plot visualizing the results from the scDist analysis. The DistPlot function creates a graphical representation of the estimated distances.

```r
DistPlot(out)
```

--------------------------------

### Apply scDist to Simulated Data

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Applies the scDist function to the simulated normalized counts and metadata. Specifies fixed and random effects, and the cluster column for analysis.

```r
out <- scDist(sim$Y,sim$meta.data,fixed.effects = "response",
              random.effects="patient",
              clusters="clusters")
```

--------------------------------

### Plot Genes Associated with Perturbation

Source: https://github.com/phillipnicol/scdist/blob/master/README.md

Generates a plot showing genes associated with a specific perturbed cluster using the distGenes function. This helps in identifying the genes driving the observed cell type changes.

```r
distGenes(out, cluster = "a")
```

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