dashboard.Rmd

---
title: "Gene Expression"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
---

```{r setup, include=FALSE}

library(knitr)
library(d3heatmap)
library(flexdashboard)
```

Column {data-width=400}
-----------------------------------------------------------------------

```{r laod-log2 transformation}
dir <- "/Users/cigom/transcriptomics/oyster_full_assembly/"
file <- 'RSEM.isoforms.counts.matrix_cv_pfam_goPfam_swissprot_pep.txt'
datExpr_annt <- read.csv(paste0(dir, file), sep = '\t')

data <- datExpr_annt[,1:21]

dataExp <- log2(data+1)
dataExp <- as.matrix(dataExp) # convert to matrix
dataExp <- t(scale(t(dataExp), scale=F)) # Centering rows

x <- as.data.frame(dataExp)
x <- head(x, n = 100)
y <- head(datExpr_annt[,-c(1:21)], n = 100)

```
### Chart A

```{r heatmap}
# make dendogram
library(dendextend)

row_dend  <- x %>% dist %>% hclust %>% as.dendrogram %>%
   set("branches_k_color", k = 3) %>% set("branches_lwd", c(1,3)) %>%
   ladderize

#    rotate_DendSer(ser_weight = dist(x))

col_dend  <- x %>% t %>% dist %>% hclust %>% as.dendrogram %>%
   set("branches_k_color", k = 2) %>% set("branches_lwd", c(1,2)) %>%
   ladderize
#    rotate_DendSer(ser_weight = dist(t(x)))

# Colorint
Samples <- sapply(strsplit(as.character(names(x)), "_"), `[`, 1)

library(heatmaply)

heatmaply(x, Rowv = row_dend, Colv = col_dend, hclust_method = 'average',
          margins = c(40,40), plot_method = "plotly",
          colors = brewer.pal(n = 9, name = "BrBG"), col_side_colors = Samples,
          fontsize_col = 5, fontsize_row = 10, 
          labRow = y$go, file = paste0(dir,'heatmap.html'))



```

### Chart B
```{r datatable}
library(DT)
#rownames(data) <- y$Gene
datatable(datExpr_annt[,-c(1:21)] , escape=1, options = list( pageLength = 10 ) )
```

Column {data-width=350}
-----------------------------------------------------------------------

### Chart C

```{r pca}
library(plotly)
pca <- prcomp(t(data), center = TRUE, scale. = FALSE)

## make a scree plot
pca.var <- pca$sdev^2
pca.var.per <- round(pca.var/sum(pca.var)*100, 1)
#
pca.data <- data.frame(Sample=rownames(pca$x),
                       X=pca$x[,1],
                       Y=pca$x[,2])

#

samples.name <- 'diffExp/metadata.file'
samples.file <- paste0(dir, samples.name)

metadata <- read.csv(samples.file, header=T, 
                     com='', check.names=F, 
                     sep='\t', stringsAsFactors = FALSE)


metadata <- metadata[!duplicated(metadata$Replica, fromLast = TRUE) ,]

#identical(rownames(pca.data), metadata$Identificador)
pca.data <- cbind(pca.data, metadata)


n <-length(unique(pca.data$Sexo))

library(RColorBrewer)

getPalette = colorRampPalette(brewer.pal(n, "Dark2"))


library(ggplot2)
pcaplot <- ggplot(data=pca.data, aes(x=X, y=Y, label=Grupo, color = Sexo)) +
  geom_text() +
  geom_point() +
  #geom_point(aes(color = factor), size = 5, alpha = 0.7) +
  xlab(paste("PC1 - ", pca.var.per[1], "%", sep="")) +
  ylab(paste("PC2 - ", pca.var.per[2], "%", sep="")) + theme_bw() +
  scale_color_manual(values=getPalette(n)) + facet_wrap(~Tejido)

ggplotly(pcaplot)
```

### Chart D 

```{r boxplot}
datam2 <- reshape2::melt(data, value.name = 'log2')
datam2$tissue <- sapply(strsplit(as.character(datam2$variable), "_"), `[`, 1)
#datam2$condition <- sapply(strsplit(as.character(datam2$variable), "_"), `[`, 1)

library(ggplot2)

p2 <- ggplot(datam2, aes(x=variable, y=log2)) + 
  geom_boxplot() + facet_wrap(~tissue, scales = 'free', nrow = 1) +
  stat_summary(fun.y=mean, geom="line", color = 'red', mapping = aes(group = tissue)) +
  labs(title="log2 Transformed data count",x="Samples (replicates)", y = "Expression") +
  theme_classic() + theme(axis.text.x = element_text(size = 9, angle = 75)) +
  coord_flip()

library(plotly)
ggplotly(p2)

# rmarkdown::run(file = '~/Documents/GitHub/infovis/dashboard.Rmd') # to launch
# rmarkdown::render(input = '~/Documents/GitHub/infovis/dashboard.Rmd') # <- to save
```