Code by TomMakesThings

About

This is part of a research project "Clustering and Topological Data Analysis of Single-Cell RNA Sequencing Data". For more information visit: https://tommakesthings.github.io/Clustering-and-TDA-of-scRNA-seq-Data/.

In this notebook, a new simulated dataset of scRNA-seq gene counts and group labels is created using the Splat simulator which is part of the R package Splatter. The dataset is seeded by mimicking gene expression from a dataset of human lung adenocarcinoma cells sc_10x by Tian et al. It is then saved as CSV files with Python.

Imports

# Allows R code to be executed using %%R before execution
%reload_ext rpy2.ipython

from google.colab import files
import pandas as pandas

%%R
# Download Splatter
install.packages("BiocManager")
BiocManager::install(version = "3.12")
BiocManager::install("splatter") # Takes long time to download

suppressPackageStartupMessages({
  library(splatter)
  library(scater)
})

Citation for Splatter and the original paper.

%%R
citation("splatter")

  Zappia L, Phipson B, Oshlack A. Splatter: Simulation of single-cell
  RNA sequencing data. Genome Biology. 2017;
  doi:10.1186/s13059-017-1305-0

A BibTeX entry for LaTeX users is

  @Article{,
    author = {Luke Zappia and Belinda Phipson and Alicia Oshlack},
    title = {Splatter: simulation of single-cell RNA sequencing data},
    journal = {Genome Biology},
    year = {2017},
    url = {http://dx.doi.org/10.1186/s13059-017-1305-0},
    doi = {10.1186/s13059-017-1305-0},
  }

Get Dataset to Replicate

Use R to download the dataset sc_10x from Luyi Tian's GitHub and open as dataset.

%%R
# URL of dataset to replicate
dataset_URL <- "https://github.com/LuyiTian/sc_mixology/blob/master/data/sincell_with_class.RData?raw=true"
connection <- gzcon(url(dataset_URL))
# Download data as SingleCellExperiment class
dataset <- get(load(connection))
dataset

class: SingleCellExperiment 
dim: 16468 902 
metadata(3): scPipe Biomart log.exprs.offset
assays(2): counts logcounts
rownames(16468): ENSG00000272758 ENSG00000154678 ... ENSG00000054219
  ENSG00000137691
rowData names(0):
colnames(902): CELL_000001 CELL_000002 ... CELL_000955 CELL_000965
colData names(15): unaligned aligned_unmapped ... cell_line_demuxlet
  demuxlet_cls
reducedDimNames(0):
altExpNames(0):

Get the gene counts from dataset and view an extract.

%%R
# Get number of features (genes) for a sample (cell)
total_genes <- dim(counts(dataset))[[1]][1]
# Get matrix of gene reads per cell sample
counts <- counts(dataset)
counts[1:5, 1:5]

                CELL_000001 CELL_000002 CELL_000003 CELL_000004 CELL_000005
ENSG00000272758           0           0           0           2           0
ENSG00000154678           0           0           0           1           0
ENSG00000148737           0           0           0           1           3
ENSG00000196968           0           0           0           1           2
ENSG00000134297           0           0           0           1           1

Create Simulated Datasets

Splat Simulation

Simulate a single population of cells that replicates the variation of sc_10x.

%%R
# Create dataset using Splat for a single population of cells
parameters <- splatEstimate(counts)
simulated <- splatSimulate(parameters, verbose = FALSE)
simulated

class: SingleCellExperiment 
dim: 16468 902 
metadata(1): Params
assays(6): BatchCellMeans BaseCellMeans ... TrueCounts counts
rownames(16468): Gene1 Gene2 ... Gene16467 Gene16468
rowData names(4): Gene BaseGeneMean OutlierFactor GeneMean
colnames(902): Cell1 Cell2 ... Cell901 Cell902
colData names(3): Cell Batch ExpLibSize
reducedDimNames(0):
altExpNames(0):

%%R
# Print extract of dataset
simulated_counts <- counts(simulated)
print(simulated_counts[1:5, 1:5])
cat("\n")

# Plot first two principal components extracted by PCA to visualise the dataset
sim <- logNormCounts(simulated)
sim <- runPCA(sim)
plotPCA(sim)

      Cell1 Cell2 Cell3 Cell4 Cell5
Gene1    13    21     7     5    28
Gene2    11     1     7     1     7
Gene3     8     0     2     2     0
Gene4     0     0     0     0     0
Gene5     0     0     0     0     0

Simulate 4 groups of cells seeded by sc_10x.

%%R
# Simulate cells in groups
simulated.groups <- splatSimulate(parameters, # Use parameters of sc_10x
                                  group.prob = c(0.4, 0.15, 0.2, 0.25), # Chance of each cell belonging to group
                                  method = "groups", # Create groups of cells rather than single
                                  verbose = FALSE, # Supress printing
                                  nGenes = total_genes, # Use same number of genes as dataset
                                  batchCells = 2000) # Create 2000 cells
# View extract of simulated data
simulated_group_counts <- counts(simulated.groups)
print(simulated_group_counts[1:5, 1:5])
cat("\n")

# Plot PCA to visualise new dataset
simulated.groups <- logNormCounts(simulated.groups)
simulated.groups <- runPCA(simulated.groups)
plotPCA(simulated.groups, colour_by = "Group")

      Cell1 Cell2 Cell3 Cell4 Cell5
Gene1     2     6     1     7     4
Gene2     0     0     0     0     0
Gene3     2     4     2     5     0
Gene4     0     0     7     0     0
Gene5     2     1     0     0     0

%%R
# View information about new dataset
simulated.groups

class: SingleCellExperiment 
dim: 16468 2000 
metadata(1): Params
assays(7): BatchCellMeans BaseCellMeans ... counts logcounts
rownames(16468): Gene1 Gene2 ... Gene16467 Gene16468
rowData names(8): Gene BaseGeneMean ... DEFacGroup3 DEFacGroup4
colnames(2000): Cell1 Cell2 ... Cell1999 Cell2000
colData names(5): Cell Batch Group ExpLibSize sizeFactor
reducedDimNames(1): PCA
altExpNames(0):

Get the group labels of each cell.

%%R
# List of group assignments for each cell
groups_metadata <- colData(simulated.groups)[,3]

Compare Simulations

Plot a graph comparing the simulated data to the original dataset based upon the distribution of mean expression. This demonstrates how well the Splatter was able to immitate the data.

%%R
# Plot graph comparing simulated and real data
comparison <- compareSCEs(list(Splat_Single = simulated, Splat_Groups = simulated.groups, sc_10x = dataset))
comparison$Plots$Means

Save Data to File

Using R, save the simulated group data and the matching labels as CSV files.

%%R
# Save as SingleCellExperiment and compressed csv
RData_file = '/simulated.RData'
csv_file = '/simulated_counts.csv.gz'
metadata_csv_file = '/simulated_metadata.csv.gz'
save(simulated.groups, file=RData_file)
write.csv(simulated_group_counts, file=gzfile(csv_file))
write.csv(groups_metadata, file=gzfile(metadata_csv_file))

Now download the files using Python.

# Download data
RData_file = '/simulated.RData' # Repeated as Python not R
csv_file = '/simulated_counts.csv.gz'
metadata_csv_file = '/simulated_metadata.csv.gz'
#files.download(RData_file)
files.download(csv_file)
files.download(metadata_csv_file)

View the simulated group data and the labels of each cell as DataFrames.

dataset = pandas.read_csv(csv_file, index_col=0)
dataset

	Cell1	Cell2	Cell3	Cell4	Cell5	Cell6	Cell7	Cell8	Cell9	Cell10	Cell11	Cell12	Cell13	Cell14	Cell15	Cell16	Cell17	Cell18	Cell19	Cell20	Cell21	Cell22	Cell23	Cell24	Cell25	Cell26	Cell27	Cell28	Cell29	Cell30	Cell31	Cell32	Cell33	Cell34	Cell35	Cell36	Cell37	Cell38	Cell39	Cell40	...	Cell1961	Cell1962	Cell1963	Cell1964	Cell1965	Cell1966	Cell1967	Cell1968	Cell1969	Cell1970	Cell1971	Cell1972	Cell1973	Cell1974	Cell1975	Cell1976	Cell1977	Cell1978	Cell1979	Cell1980	Cell1981	Cell1982	Cell1983	Cell1984	Cell1985	Cell1986	Cell1987	Cell1988	Cell1989	Cell1990	Cell1991	Cell1992	Cell1993	Cell1994	Cell1995	Cell1996	Cell1997	Cell1998	Cell1999	Cell2000
Gene1	2	6	1	7	4	2	0	1	1	2	0	4	6	3	0	4	0	1	0	4	1	6	7	1	6	8	10	0	1	4	0	1	5	3	0	5	1	6	4	3	...	1	0	1	5	2	2	2	2	3	4	1	3	3	14	1	1	4	2	0	0	12	13	1	7	1	0	1	0	0	8	2	4	4	1	0	2	0	2	0	1
Gene2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	2	1	1	1	0	0	0	0	0	0	...	0	0	0	1	0	0	0	0	0	0	0	0	0	2	0	0	1	0	2	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0
Gene3	2	4	2	5	0	7	0	2	0	6	0	1	2	1	1	0	0	1	0	1	0	9	4	4	0	1	1	4	1	2	1	7	2	3	5	3	0	4	6	4	...	5	1	1	1	0	8	0	0	3	0	0	1	8	3	8	5	1	3	2	1	2	3	0	6	0	1	0	2	0	5	2	1	5	9	1	0	2	0	7	1
Gene4	0	0	7	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	3	0	2	1	0	0	1	0	1	0	1	8	1	1	0	1	0	3	1	0	3	2	...	0	0	0	1	0	0	1	0	0	0	1	0	1	14	0	3	4	0	0	5	1	2	0	2	0	0	1	1	0	2	4	0	1	0	3	4	0	0	0	0
Gene5	2	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	3	0	0	0	4	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	2	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Gene16464	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0
Gene16465	1	4	0	0	2	6	2	9	0	8	1	14	6	4	7	4	1	9	4	0	2	7	14	0	0	4	5	3	5	5	4	10	5	7	3	6	0	0	11	4	...	8	0	2	2	3	1	2	3	2	8	0	1	3	16	4	1	3	2	3	2	1	23	0	5	2	5	13	1	3	10	5	1	8	3	10	2	11	3	22	4
Gene16466	5	12	1	8	5	7	0	7	1	7	2	3	12	5	7	8	1	2	0	0	2	14	11	3	9	5	0	4	11	1	0	0	1	35	1	0	6	14	2	5	...	13	0	6	0	10	8	1	3	5	11	0	4	4	9	0	3	3	7	16	9	1	21	1	2	2	10	0	2	7	1	3	4	7	7	1	2	13	10	5	2
Gene16467	0	5	0	0	0	1	0	0	5	2	5	0	0	0	0	2	0	0	0	0	2	2	1	0	0	0	7	0	2	5	2	3	0	6	0	0	0	0	0	0	...	5	0	1	1	0	0	0	3	4	1	7	2	5	0	0	0	3	0	0	0	0	1	0	2	0	0	0	0	0	0	1	1	0	0	0	0	0	2	0	1
Gene16468	3	0	0	0	1	2	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	2	0	2	0	4	0	0	0	0	...	2	0	0	0	0	0	0	0	0	1	0	0	3	0	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	0

16468 rows × 2000 columns

metadata = pandas.read_csv(metadata_csv_file, index_col=0)
metadata

	x
1	Group4
2	Group3
3	Group1
4	Group3
5	Group2
...	...
1996	Group3
1997	Group1
1998	Group1
1999	Group3
2000	Group4

2000 rows × 1 columns