Loyale

# Here is the workflow in a nutshell:
#
# 1) Annotate cells in your favorite single cell framework or system
# 2) Perform your favorite dimensionality reduction into 3D.
# 3) Export annotation and 3D coordinates to .csv.
# 4) I imported these data into Blender and created the objects using the below ‘retina_3d_umap_blender.py’ script.  Some of it is automated, some of it is hard-coded and project specific.
#   - For each age (since cells here are colored by developmental age) I create a new mesh and add each datapoint for a given age to the mesh as a vertex.
# 5) I then created a single ’sample object’ for each mesh consisting of a UV sphere primitive.  Added a material to that to match the color scheme I had selected.  These were the ’templates’ to be used and applied to each vertex in the mesh using a particle system
# 6) Next created a particle system for each age with the params indicated in the python script.
# 7)  Finally I created an empty parent object to group all of the particles so I could track wi

Loyale

import bpy
import bmesh
import random
import csv

# Set render engine to cycles
bpy.context.scene.render.engine = 'CYCLES'

#Read in retina UMAP data
filename = "retina_pdata.csv"

Loyale

#!/usr/bin/etc python
from numpy.random import choice
from itertools import count,chain
from copy import deepcopy
import sys

###############
# Globals
###############
possible_states = ["progenitor","differentiated"]

Loyale

library(ggplot2)
library(grid)


#############
#Utility Functions
#############
# multiplot <- function(..., plotlist=NULL, cols) {
#     require(grid)
# 

Loyale

library(cummeRbund)
library(limma)
library(GSA)

cuff<-readCufflinks()

Input_df<-data.frame("gene_id"=rownames(dat),"gene_short_name"=rownames(dat))
Input_df<-cbind(Input_df,fpkmMatrix(genes(cuff)))
Input_df$gene_short_name<-toupper(Input_df$gene_short_name)

Loyale

require(ggplot2)
require(grid)
require(scales)

ternary <- function(t, l, r, clr = clr, pts = pts, labels = c("top", "left", "right"), showGrid = TRUE){
    library(ggplot2)
    
    ddf <- data.frame(t, l, r)
    
    # convert points to cartesian coordinates