Notebook

DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics¶

Leiden Notebook¶

Leiden Clustering of CTCs¶

Required Packages¶

In [1]:

library(reticulate)
reticulate::install_miniconda()
reticulate::use_condaenv()
py_install("python-igraph")
py_install("numpy")
py_install("leidenalg", forge = TRUE)
library(DIscBIO)
library(leiden)
library(M3Drop)

* Downloading 'https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh' ...

* Installing Miniconda -- please wait a moment ...

* Miniconda has been successfully installed at '/home/jovyan/.local/share/r-miniconda'.

'/home/jovyan/.local/share/r-miniconda'

Loading required package: SingleCellExperiment

Loading required package: SummarizedExperiment

Loading required package: MatrixGenerics

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Welcome to Bioconductor

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conda environment r-reticulate installed

python modules igraph and leidenalg installed

Loading required package: numDeriv

Required data¶

In [2]:

load("SC.RData")           # Loading the "SC" object 
filteredDataset<-SC@fdata
xprs <- as.matrix(filteredDataset)
xprs[xprs == min(xprs)] <- 0
top.feats <- M3Drop::M3DropFeatureSelection(expr_mat = xprs, mt_method = "fdr",suppress.plot = FALSE, mt_threshold = 0.5) ## Fits a Michaelis-Menten function to the dropout-rate.
top.feats <- head(top.feats$Gene, 500)
top.xprs <- xprs[top.feats, ]
Adj <- stats::cor(top.xprs)
QNT <- quantile(Adj, probs = c(0.5))
bin.Adj <- (Adj > QNT) + 0

Leiden clustering¶

The Leiden clustering algorithm has the ability for identifying high-quality partitions and it is partly based on the smart local move algorithm. The Leiden algorithm uses a fast local move procedure. It is considerably more complex than the Louvain algorithm. The resolution parameter controls the coarseness of the clusters and it can fine-tune the number of clusters to be detected.

resolution_parameter = 0.5¶

In [3]:

lei.clusts <- leiden(bin.Adj, resolution_parameter = 0.5)   # Leiden algorithm
names(lei.clusts) <- colnames(bin.Adj)

### Visualizing the clusters
SC@kmeans$kpart<-lei.clusts
Factor<-factor(lei.clusts)
K<- length(levels(Factor))
plottSNE(SC)

Evaluating the stability and consistancy of the clusters¶

In [4]:

# Silhouette plot
options(repr.plot.width=12, repr.plot.height=25)
plotSilhouette(SC,K=K)

In [5]:

# Jaccard Similarity
options(repr.plot.width=10, repr.plot.height=12)
Jaccard(SC, K=K, plot = TRUE)

0.563
0.381

resolution_parameter = 1¶

In [6]:

lei.clusts <- leiden(bin.Adj, resolution_parameter = 1)   # Leiden algorithm
names(lei.clusts) <- colnames(bin.Adj)

### Visualizing the clusters
SC@kmeans$kpart<-lei.clusts
Factor<-factor(lei.clusts)
K<- length(levels(Factor))

options(repr.plot.width=6, repr.plot.height=6)
plottSNE(SC)

** DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics**¶