# 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: ``` r # 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. ``` r 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.

SCIBER0.2.2 package

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SCIBER