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SCIBER

SCIBER is a simple method that outputs the batch-effect corrected expression data in the original space/dimension. These expression data of individual genes can be directly used for all follow-up analyses. SCIBER has four steps; each step has a clear biological meaning, and the algorithms used for them are k-means clustering, t-test, Fisher’s exact test, and linear regression, respectively, all of which are easily comprehensible

Installation

You can install the development version of SCIBER with the following instructions:

# install.packages("devtools")
devtools::install_github("RavenGan/SCIBER")

Example

The following example uses the pre-processed Human dendritic cell dataset [1] to perform batch integration.

Please note that for each data frame in the object meta, there should be two columns named cell_id and cell_type. For instance, let meta_i be a data frame under meta, and there should be two columns meta_i$cell_id and meta_i$cell_type. If the cell type information is not available, any values put in meta_i$cell_type should work.

library(SCIBER)
rm(list = ls())
set.seed(7)
data(HumanDC)
exp <- HumanDC[["exp"]]
meta <- HumanDC[["metadata"]]

# Specify the proportion for each query batch to integrate batches.
omega <- c()
omega[[1]] <- 0.6

res <- SCIBER(input_batches = exp, ref_index = 1,
batches_meta_data = meta, omega = omega, n_core = 1)
#> [1] "The available number of cores is 10. SCIBER uses 1 to perform batch effect removal."

Dataset reference

  1. Villani, A. C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., … & Hacohen, N. (2017). Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science, 356(6335), eaah4573.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.