| Title: | A Single Cell Transcriptomics Based Deconvolution Pipeline for Leukemia |
|---|---|
| Description: | Given a bulk transcriptomic (RNA-seq) sample of an Myeloid Leukemia patient calculates immune composition and drug resistance for different small-molecule inhibitors. Published in <https://www.nature.com/articles/s41698-024-00596-9>. |
| Authors: | E Onur Karakaslar [aut, cre], Redmar van den Berg [ctb] |
| Maintainer: | E Onur Karakaslar <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.1 |
| Built: | 2024-11-12 06:10:42 UTC |
| Source: | https://github.com/eonurk/seamless |
Provides a command line interface to run seAMLess deconvolution analysis on bulk RNA-seq data.
cli()cli()
List of validated command line options
TCGA-LAML bulk RNA-seq data downloaded from GDC
data(exampleTCGA)data(exampleTCGA)
An object of class data.frame with 60483 rows and 21 columns.
TCGA-LAML example data meta file downloaded from GDC
data(exampleTCGAmeta)data(exampleTCGAmeta)
An object of class data.frame with 20 rows and 34 columns.
Grch38
data(grch38)data(grch38)
An object of class tbl_df (inherits from tbl, data.frame) with 67495 rows and 3 columns.
A minimal seAMLess result list object
data(minRes)data(minRes)
An object of class list of length 2.
Given the count matrices of bulk-RNA samples, this function deconvolutes each sample into its cell types using a healthy BM reference, and calculates the sample's in vitro resistance to Venetoclax.
seAMLess( mat, scRef = seAMLessData::scRef, scRef.sample = "Sample", scRef.label = "label.new", verbose = TRUE )seAMLess( mat, scRef = seAMLessData::scRef, scRef.sample = "Sample", scRef.label = "label.new", verbose = TRUE )
mat |
count matrix (genes by 1+samples). |
scRef |
reference matrix for single cell data |
scRef.sample |
column name for the samples in single cell reference |
scRef.label |
column name for the cell names in single cell reference |
verbose |
prints detailed messages |
List of deconvoluted cell type percentages and predicted drug resistances
Given the immune compositions (ICs) of bulk-RNA samples, this function creates a ternary plot similar to ALOT tube from EuroFlow analysis and Figure 1E of our paper.
ternaryPlot(res)ternaryPlot(res)
res |
seAMLess object. |
ggplot2 object
library(seAMLess) data(minRes) ternaryPlot(minRes)library(seAMLess) data(minRes) ternaryPlot(minRes)
Trained RF model on Venetoclax Resistance
data(venoModel)data(venoModel)
An object of class randomForest of length 17.
returns a printing function to be used with in the script
verboseFn(verbose)verboseFn(verbose)
verbose |
boolean, determines whether the output going be printed or not |
print function
# Prints output verbosePrint <- verboseFn(TRUE) verbosePrint("Hello World!") # > "Hello World!" # Does not print verbosePrint <- verboseFn(FALSE) verbosePrint("Hello World!")# Prints output verbosePrint <- verboseFn(TRUE) verbosePrint("Hello World!") # > "Hello World!" # Does not print verbosePrint <- verboseFn(FALSE) verbosePrint("Hello World!")
removes ERCC peaks and duplicated genes
wrangleMat(mat)wrangleMat(mat)
mat |
pre-filters and orders bulk rna-seq data |
filtered and ordered count-matrix
library(seAMLess) data("exampleTCGA") exampleTCGA <- wrangleMat(exampleTCGA)library(seAMLess) data("exampleTCGA") exampleTCGA <- wrangleMat(exampleTCGA)