Optional_3D_plotting2.Rmd

---
title: "Practical: 3D plotting 2"
subtitle: "Transcriptome Analysis Workshop"
author: "Adam Cribbs"
date: "24/01/2022"
output:
  html_document:
    theme: cosmo
    toc: yes
---

```{r, out.width = "40%", echo=FALSE}
htmltools::img(src = knitr::image_uri(file.path("logo.png")), 
               alt = 'logo', 
               style = 'position:absolute; top:0; right:0; padding:10px;',
               width='300')

```


```{r}
#install.packages('plotly')
library(plotly)
library(Seurat)
```


```{r}
yourseuratobject <- readRDS("seurat_object.rds")

yourseuratobject <- RunTSNE(yourseuratobject,
                        reduction.use = "pca",
                        dims.use = 1:10,
                        dim.embed = 3)

tsne_1 <- yourseuratobject[["tsne"]]@cell.embeddings[,1]
tsne_2 <- yourseuratobject[["tsne"]]@cell.embeddings[,2]
tsne_3 <- yourseuratobject[["tsne"]]@cell.embeddings[,3]

```


# Prepare a dataframe for cell plotting

```{r}
plot.data <- FetchData(object = yourseuratobject, vars = c("tSNE_1", "tSNE_2", "tSNE_3", "seurat_clusters"))
```

```{r}
# Make a column of row name identities (these will be your cell/barcode names)
plot.data$label <- paste(rownames(plot.data))

# Plot your data, in this example my Seurat object had 21 clusters (0-20)
plot_ly(data = plot.data, 
        x = ~tSNE_1, y = ~tSNE_2, z = ~tSNE_3, 
        color = ~seurat_clusters, 
        colors = c("lightseagreen",
                   "gray50",
                   "darkgreen",
                   "red4",
                   "red",
                   "turquoise4",
                   "black",
                   "yellow4",
                   "royalblue1",
                   "lightcyan3",
                   "peachpuff3",
                   "khaki3",
                   "gray20",
                   "orange2",
                   "royalblue4",
                   "yellow3",
                   "gray80",
                   "darkorchid1",
                   "lawngreen",
                   "plum2",
                   "darkmagenta"),
        type = "scatter3d", 
        mode = "markers", 
        marker = list(size = 5, width=2), # controls size of points
        text=~label, #This is that extra column we made earlier for which we will use
        hoverinfo="text") #When you visualize your plotly object, hovering your mouse pointer over a point shows cell names

```

```{r}
# Say you wanto make a gene-expression 3D plot, where you can plot gene expression against a color scale
# Here using the same seurat object as above, we extract gene expression information for beta-actin 'ACTB'
# create a dataframe
plotting.data <- FetchData(object = yourseuratobject, vars = c("tSNE_1", "tSNE_2", "tSNE_3", "ACTB"))

# Say you want change the scale, so that every cell having an expression >1 will be one color
# Basically, you are re-adjusting the scale here, so that any cell having a certain expression 
# will light up on your 3D plot

# First make another column in your dataframe, where all values above 1 are re-assigned a value of 1
# This information is stored in the 'changed' column of your dataframe
plotting.data$changed <- ifelse(test = plotting.data$ACTB <1, yes = plotting.data$ACTB, no = 1)

# Add the label column, so that now the column has 'cellname-its expression value'
plotting.data$label <- paste(rownames(plotting.data)," - ", plotting.data$ACTB, sep="")

# Plot your data, in this example my Seurat object had 21 clusters (0-20), and cells express a gene called ACTB
plot_ly(data = plotting.data, 
        x = ~tSNE_1, y = ~tSNE_2, z = ~tSNE_3, 
        color = ~changed, # you can just run this against the column for the gene as well using ~ACTB, the algorith will automatically scale in that case based on maximal and minimal values
        opacity = .5,
        colors = c('darkgreen', 'red'), 
        type = "scatter3d", 
        mode = "markers",
        marker = list(size = 5, width=2), 
        text=~label,
        hoverinfo="text"
)

```