| Type: | Package |
| 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. |
| Version: | 0.1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Biobase, ggplot2, optparse, data.table, |
| Suggests: | MuSiC, ggtern, seAMLessData, randomForest |
| Additional_repositories: | https://eonurk.github.io/drat/ |
| URL: | https://github.com/eonurk/seAMLess |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2024-11-11 10:18:00 UTC; onur-lumc |
| Author: | E Onur Karakaslar [aut, cre], Redmar van den Berg [ctb] |
| Maintainer: | E Onur Karakaslar <eonurkara@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2024-11-11 12:50:02 UTC |
Command Line Interface for seAMLess
Description
Provides a command line interface to run seAMLess deconvolution analysis on bulk RNA-seq data.
Usage
cli()
Value
List of validated command line options
TCGA-LAML bulk RNA-seq data downloaded from GDC
Description
TCGA-LAML bulk RNA-seq data downloaded from GDC
Usage
data(exampleTCGA)
Format
An object of class data.frame with 60483 rows and 21 columns.
TCGA-LAML example data meta file downloaded from GDC
Description
TCGA-LAML example data meta file downloaded from GDC
Usage
data(exampleTCGAmeta)
Format
An object of class data.frame with 20 rows and 34 columns.
Grch38
Description
Grch38
Usage
data(grch38)
Format
An object of class tbl_df (inherits from tbl, data.frame) with 67495 rows and 3 columns.
A minimal seAMLess result list object
Description
A minimal seAMLess result list object
Usage
data(minRes)
Format
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.
Description
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.
Usage
seAMLess(
mat,
scRef = seAMLessData::scRef,
scRef.sample = "Sample",
scRef.label = "label.new",
verbose = TRUE
)
Arguments
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 |
Value
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.
Description
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.
Usage
ternaryPlot(res)
Arguments
res |
seAMLess object. |
Value
ggplot2 object
Examples
library(seAMLess)
data(minRes)
ternaryPlot(minRes)
Trained RF model on Venetoclax Resistance
Description
Trained RF model on Venetoclax Resistance
Usage
data(venoModel)
Format
An object of class randomForest of length 17.
verboseFn
Description
returns a printing function to be used with in the script
Usage
verboseFn(verbose)
Arguments
verbose |
boolean, determines whether the output going be printed or not |
Value
print function
Examples
# 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
Description
removes ERCC peaks and duplicated genes
Usage
wrangleMat(mat)
Arguments
mat |
pre-filters and orders bulk rna-seq data |
Value
filtered and ordered count-matrix
Examples
library(seAMLess)
data("exampleTCGA")
exampleTCGA <- wrangleMat(exampleTCGA)