10-activity-analysis.Rmd

# Actvity-based analysis

The **driver activity estimation** is one of the most important features of scMINER. **Mathematically**, the activity of one driver is a type of mean of the expressions of its targets. And **biologically**, the activity can be interpreted as a measure that describes how actively the driver functions, like the enzymes in digesting their subtracts, kinase in activating their downstream genes. Given the gene expression profiles and networks, scMINER can estimate the activities of some predefined drivers, including not only transcription factors (TFs) but also signaling genes (SIGs). scMINER provides a few functions to effortlessly calculate the activities, identify the hidden drivers and visualize them in multiple ways.

## Calculate the activities

scMINER provides two functions, `getActivity_individual()` and `getActivity_inBatch()`, to calculate the driver activities.

### Calculate activities per group

`getActivity_individual()` is designed to calculate the activities per group. It takes the network files as the input:

```{r calculate-activity-individually, eval=FALSE}
## let's use B cell as an example
activity_B.eset <- getActivity_individual(input_eset = pbmc14k_log2cpm.eset[, pData(pbmc14k_log2cpm.eset)$trueLabel == "B"],
                                          network_file.tf = system.file("extdata/demo_pbmc14k/SJARACNe/B/TF/bt100_pc001/consensus_network_ncol_.txt", package = "scMINER"),
                                          network_file.sig = system.file("extdata/demo_pbmc14k/SJARACNe/B/SIG/bt100_pc001/consensus_network_ncol_.txt", package = "scMINER"),
                                          driver_type = "TF_SIG")
```

### Calculate activities in batch

If you need to calculate the activity for multiple groups, this is usually the case, you can do it using `getActivity_individual()` as shown above one by one and merge the esets after that. Or, scMINER privides another function, `getActivity_inBatch()`, to calculate the activity in batch:

```{r calculate-activity-in-batch}
## let's use B cell as an example
activity.eset <- getActivity_inBatch(input_eset = pbmc14k_log2cpm.eset, sjaracne_dir = system.file("extdata/demo_pbmc14k/SJARACNe", package = "scMINER"),  group_name = "trueLabel", driver_type = "TF_SIG", activity_method = "mean", do.z_normalization = TRUE)
```

### Save activity eset object

```{r save-activity-eset, eval=FALSE}
saveRDS(activity.eset, file = "/your-path/PBMC14k/DATA/activity.eset")
```


## Differential activity analysis

Similar to `getDE()`, scMINER provides a function, `getDA()`, to perform the differential activity analysis and identify the group-specific drivers.

```{r differential-activity-analysis-1}
## 1. To perform differential expression analysis in a 1-vs-rest manner for all groups
da_res1 <- getDA(input_eset = activity.eset, group_by = "cell_type", use_method = "t.test")
head(da_res1)
```

```{r differential-activity-analysis-2, eval=FALSE}
## 2. To perform differential expression analysis in a 1-vs-rest manner for one specific group
da_res2 <- getDA(input_eset = activity.eset, group_by = "cell_type", g1 = c("B"), use_method = "t.test")

## 3. To perform differential expression analysis in a rest-vs-1 manner for one specific group
da_res3 <- getDA(input_eset = activity.eset, group_by = "cell_type", g0 = c("B"), use_method = "t.test")

## 4. To perform differential expression analysis in a 1-vs-1 manner for any two groups
da_res4 <- getDA(input_eset = activity.eset, group_by = "cell_type", g1 = c("CD4Treg"), g0 = c("CD4TCM"), use_method = "t.test")
```

The `getTopFeatures()` function can aslo be used to easily extract the group-specific markers from the differential expression result:
```{r get-top-drivers}
top_drivers <- getTopFeatures(input_table = da_res1, number = 10, group_by = "g1_tag", sort_by = "log2FC", sort_decreasing = TRUE)
dim(top_drivers)
head(top_drivers)
```