### Install SPsimSeq from GitHub

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Installs the SPsimSeq package directly from its GitHub repository using the devtools package. Ensure devtools is installed first.

```r
library(devtools)
install_github("CenterForStatistics-UGent/SPsimSeq")
```

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

### Load SPsimSeq and Check Version

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Loads the SPsimSeq package and prints its currently installed version. This is a good practice to ensure the correct version is loaded.

```r
suppressPackageStartupMessages(library(SPsimSeq))
cat("SPsimSeq package version", 
    as.character(packageVersion("SPsimSeq")), "\n")
```

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

### Install SPsimSeq from Bioconductor

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Installs the SPsimSeq package from Bioconductor using the BiocManager package. This is the recommended method for stable releases.

```r
library(BiocManager)
BiocManager::install("SPsimSeq")
```

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

### Simulate Bulk RNA-seq Data

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Simulates bulk RNA-seq count data using the SPsimSeq function. This example configures simulation parameters such as the number of genes, samples, groups, differential expression proportion, and fold-change threshold.

```r
# load the Zhang bulk RNA-seq data
data("zhang.data.sub") 
# filter genes with sufficient expression (important step) 
zhang.counts <- zhang.data.sub$counts[rowSums(zhang.data.sub$counts > 0)>=5, ]  
# We simulate only a single data (n.sim = 1) with the following property
# - 2000 genes ( n.genes = 2000)
# - 20 samples (tot.samples = 20)
# - the samples are equally divided into 2 groups each with 90 samples 
#   (group.config = c(0.5, 0.5))
# - all samples are from a single batch (batch = NULL, batch.config = 1)
# - we add 10% DE genes (pDE = 0.1) 
# - the DE genes have a log-fold-change of at least 0.5 in 
#   the source data (lfc.thrld = 0.5)
# - we do not model the zeroes separately, they are the part of density 
#    estimation (model.zero.prob = FALSE)
# simulate data
set.seed(6452)
zhang.counts2 <- zhang.counts[sample(nrow(zhang.counts), 2000), ]
sim.data.bulk <- SPsimSeq(n.sim = 1, s.data = zhang.counts2,
                          group = zhang.data.sub$MYCN.status, n.genes = 2000, 
                          batch.config = 1,
                          group.config = c(0.5, 0.5), tot.samples = 20, 
                          pDE = 0.1, lfc.thrld = 0.5, 
                          result.format = "list")
```

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

### Inspect Simulated Bulk RNA-seq Sample Information

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Displays the first few rows of the sample metadata generated during the bulk RNA-seq simulation. This includes batch, group, and library size information for each sample.

```r
head(sim.data.bulk1$colData)               # sample info
```

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

### Simulate Single-Cell RNA-Seq Data

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Simulates a single-cell RNA-Seq dataset with specified parameters. This includes setting the number of genes, cells, group configurations, batch information, proportion of differentially expressed genes, log-fold-change threshold, zero probability modeling, and output format. Requires the SingleCellExperiment package.

```r
suppressPackageStartupMessages(library(SingleCellExperiment))
data("scNGP.data")
scNGP.data2 <- scNGP.data[rowSums(counts(scNGP.data) > 0)>=5, ]
treatment <- ifelse(scNGP.data2$characteristics..treatment=="nutlin",2,1)
set.seed(654321)
scNGP.data2 <- scNGP.data2[sample(nrow(scNGP.data2), 2000), ]
sim.data.sc <- SPsimSeq(n.sim = 1, s.data = scNGP.data2,
                        group = treatment, n.genes = 2000, batch.config = 1,
                        group.config = c(0.5, 0.5), tot.samples = 100, 
                        pDE = 0.1, lfc.thrld = 0.5, model.zero.prob = TRUE,
                        result.format = "SCE")
```

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

### Inspect Simulated Bulk RNA-seq Counts

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Displays the first few rows and columns of the simulated gene count data from the bulk RNA-seq simulation. This helps in verifying the structure and initial values of the simulated counts.

```r
sim.data.bulk1 <- sim.data.bulk[[1]]                              
head(sim.data.bulk1$counts[, seq_len(5)])  # count data
```

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

### Inspect Simulated Gene Counts

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Displays the first few rows and columns of the raw counts matrix within the simulated SingleCellExperiment object. This provides a glimpse of the gene expression values for the initial cells.

```r
head(counts(sim.data.sc1)[, seq_len(5)])
```

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

### Access Simulated Data from SingleCellExperiment Object

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Extracts the simulated data as a SingleCellExperiment object from the list returned by SPsimSeq. This allows for inspection of the simulated counts, cell metadata, and gene annotations.

```r
sim.data.sc1 <- sim.data.sc[[1]]
class(sim.data.sc1)
```

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

### Inspect Cell Metadata

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Retrieves and displays the column data (metadata) for the cells in the simulated SingleCellExperiment object. This includes information on batch, group assignment, and simulated library size.

```r
colData(sim.data.sc1)
```

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

### Inspect Simulated Bulk RNA-seq Gene Information

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Displays the first few rows of the gene metadata generated during the bulk RNA-seq simulation. This includes whether a gene was designated as differentially expressed (DE.ind) and its source ID.

```r
head(sim.data.bulk1$rowData)               # gene info
```

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

### Inspect Gene Annotations

Source: https://github.com/centerforstatistics-ugent/spsimseq/blob/master/README.md

Retrieves and displays the row data (annotations) for the genes in the simulated SingleCellExperiment object. This indicates whether a gene was marked as differentially expressed (DE.ind) and its source ID.

```r
rowData(sim.data.sc1)
```

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