RECODE

Resolution of the curse of dimensionality (RECODE) is a noise reduction method for single-cell sequencing data based on high-dimensional statistics.

Y. Imoto, T. Nakamura, et al. Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis, 2022, Life Science Alliance.

The license gives permission for personal, academic, or educational use. Any commercial use is strictly prohibited. Please contact imoto.yusuke.4e<at>kyoto-u.ac.jp for licensing terms for any commercial use.

Github Project

Python code

Installation

To install RECODE package, use pip as follows:

$ pip install screcode

PyPi downloads (by PePy)

Documentation

Tutorials and API reference

Requirements

Python3
numpy
scipy
scikit-learn

R code

Remark: The current version of the R code is not fast because of the lower speed of the PCA algorithm on R. Therefore, we recommend using the python code for large-scale data.

Installation

You can install RECODE on R with:

devtools::install_github("yusuke-imoto-lab/RECODE/R")

Tutorials

For the single-cell sequeincing data X (rows: genes/epigenomes, columns: cells), we can apply RECODE as follows.

library(RECODE)

X.RECODE <- RECODE(X)

In the Seurat analysis, we can apply RECODE to SeuratObject and set it as default, as follows:

library(RECODE)
library(Matrix)

data <- as.matrix(seurat[["RNA"]]@counts)
data_RECODE <- RECODE(data)
seurat[["RECODE"]] <- CreateAssayObject(counts = Matrix(data_RECODE, sparse = TRUE))
DefaultAssay(seurat) <- "RECODE"

For a detailed analysis, please see below:

Tutorial (Run)

Tutorial (Run, QC, Clustering, Annotating etc.)

R code (Python calling)

Installation

After installing remotes (install.packages("remotes")), you can install “recodeinstaller” with the following command:

remotes::install_github("yusuke-imoto-lab/recodeinstaller")

Then, the following command installs the Python version of RECODE.

library(recodeinstaller)

install_screcode()

Regarding the detail of installer, please see recodeinstaller.

Tutorials

For the single-cell sequeincing data X (rows: genes/epigenomes, columns: cells), we can apply RECODE as follows.

library(reticulate)

source("recodeloader/load_recodeenv.R")

plt <- reticulate::import(module="matplotlib.pyplot")
screcode <- reticulate::import(module="screcode.screcode")
X.RECODE<-screcode$RECODE(X)

Below is a more detailed analysis:

Tutorial (Python calling)

Desktop Application

Installation and Tutorials

Windows (exe) and MAC OS (dmg) applications are available.