# install

This package now is released at github.com, and you can install it, as follows:

```{r}
devtools::install_github("JPingAMMS/tidyOmics")
```

The related dependency packages will also be installed.

# Usage

## Example using dataset with 4 omics and 3 time points, from AMS dataset.

### load packages

```{r}
# packages
library(tidyOmics)

```

### Read-in data

#### omics data

```{r}
# omics dataframe
load(file = "E:/pj/r_package_publish/tidyOmics_data/omics_mat.RData")
head(omic3)[,1:5]
```

#### phenotype data

```{r}
# phenotype dataframe
load(file = "E:/pj/r_package_publish/tidyOmics_data/pheno_mat.RData")
head(pheno)[,1:5]
```

```{r}
samplelist_base  = pheno %>% filter(group=="Base") %>% pull(uniqueID)
samplelist_day1  = pheno %>% filter(group=="Day1") %>% pull(uniqueID)
samplelist_day90 = pheno %>% filter(group=="Day90") %>% pull(uniqueID)

samplelist_base_in_omic3 = intersect(samplelist_base, names(omic3))
samplelist_day1_in_omic3 = intersect(samplelist_day1, names(omic3))
samplelist_day90_in_omic3 = intersect(samplelist_day90, names(omic3))

omic3_base  = omic3 %>% select(symbol, all_of(samplelist_base_in_omic3))
omic3_day1  = omic3 %>% select(symbol, all_of(samplelist_day1_in_omic3))
omic3_day90 = omic3 %>% select(symbol, all_of(samplelist_day90_in_omic3))

plot_cor_heatmap_pheno(omic_df = omic3,
                       pheno_df = pheno,
                       gene_int = "MCUR1",
                       pheno_int = NA,
                       method="pearson")

plot_cor_heatmap_pheno(omic_df = omic3_day90,
                       pheno_df = pheno,
                       gene_int = "MCUR1",
                       pheno_int = NA,
                       method="pearson")

```

## Example using RNA_tpm and pheno from in-house T102 dataset

### Read-in data

```{r}
load("E:/pj/r_package_publish/zhou-T102/T102_omics_mat.RData")
load("E:/pj/r_package_publish/zhou-T102/T102_pheno_mat.RData")
```

### associations between MCM2 gene and all available phenotypes.

```{r}
plot_cor_heatmap_pheno(omic_df = omic3,
                       pheno_df = pheno,
                       gene_int = "MCM2",
                       pheno_int = NA,
                       method="pearson")
```

### associations between MCM2 gene and HGB.

```{r}
var1_df = omic3 %>% filter(symbol=="MCM2")
var2_df = pheno %>% select(uniqueID, HGB)
  # var2_df = omic3 %>% filter(symbol == "HIF1A")
  # var2_df = pheno %>% select(uniqueID, DBP)

plot_cor_line(var1_df = var1_df,
              var2_df = var2_df,
              method="pearson")

```