README.md

In zhangwenjie-tina/scibetR:

scibetR

Pure R version of scibet, a portable and fast single cell type identifier. It takes longer than the original functions in scibet.

Installation Guide

Installing scibetR To install scibetR, run:

if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
devtools::install_github("zwj-tina/scibetR")

Tutorial

Library

library(ggplot2)
library(tidyverse)
library(scibetR)
library(viridis)
library(ggsci)

Load the data

For expression matrix (TPM), rows should be cells and columns should be genes but the last column should be "label" for each cell.

path_da <- "~/test.rds.gz"
expr <- readr::read_rds(path = path_da) 

E(ntropy)-test for supervised gene selection

etest_gene <- SelectGene_R(expr, k = 50)
etest_gene

scibetR: Single Cell Identifier Based on Entropy Test

1. For reference set, rows should be cells, column should be genes and the last column should be "label" (TPM).
2. For query set, rows should be cells and column should be genes (TPM). example:

tibble(
  ID = 1:nrow(expr),
  label = expr$label
) %>%
  dplyr::sample_frac(0.7) %>%
  dplyr::pull(ID) -> ID

train_set <- expr[ID,]      #construct reference set
test_set <- expr[-ID,]      #construct query set

prd <- SciBet_R(train_set, test_set[,-ncol(test_set)])

False positive control

Due to the incomplete nature of reference scRNA-seq data collection, cell types excluded from the reference dataset may be falsely predicted to be a known cell type. By applying a null dataset as background, SciBet controls the potential false positives while maintaining high prediction accuracy for cells with types covered by the reference dataset (positive cells). For the purposes of this example, these three datasets are used to get started.

null <- readr::read_rds('~/null.rds.gz')
reference <- readr::read_rds('~/reference.rds.gz')
query <- readr::read_rds('~/query.rds.gz')

For query set, “negative cells” account for more than 60%.

ori_label <- query$label
table(ori_label)

The confidence score of each query cell is calculated with the function conf_score_R.

query <- query[,-ncol(query)]
c_score <- conf_score_R(ref = reference, query = query, null_expr = null, gene_num = 500)

Entropy calculation

Compute expression entropy. expr,The expression dataframe. Rows should be cells and columns should be genes. window, The window size for expression value discretization. low The lower limit for normalizing expression entropy

ent_res <- Entropy_R(expr,window=120,low=2000)

return

# A tibble: 11,516 x 5
   gene  mean.expr entropy    fit norm_ent
   <chr>     <dbl>   <dbl>  <dbl>    <dbl>
 1 A2M       63.2    1.26  0.191    0.248 
 2 AAAS      73.9    1.03  0.210    0.204 
 3 AACS       8.73   0.412 0.0419   0.0813
 4 AAED1     37.8    0.631 0.136    0.124 
 5 AAGAB     65.7    1.18  0.196    0.233 
 6 AAK1      13.8    0.683 0.0622   0.135 
 7 AAMDC     13.1    0.414 0.0596   0.0817
 8 AAMP     159.     1.63  0.316    0.322 
 9 AAR2      49.3    0.952 0.163    0.188 
10 AARS      39.6    0.922 0.141    0.182 
# … with 11,506 more rows

LoadModel

x, A SciBet model in the format of a matrix as the trained reference.To facilitate matrix multiplication in the process, its rows are genes and columns are labels.

y <- LoadModel_R(x,genes=NULL,labels=Null)

return a function as

Bet_R <- function(expr, result="list"){
}

correction

add %>% in line 312 in Marker_heatmap() function

zhangwenjie-tina/scibetR documentation built on July 6, 2023, 2:14 p.m.