scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq).
scMAGeCK is based on our previous MAGeCK and MAGeCK-VISPR models for pooled CRISPR screens, but further extends to scRNA-seq as the readout of the screening experiment. scMAGeCK consists of two modules: scMAGeCK-Robust Rank Aggregation (RRA), a sensitive and precise algorithm to detect genes whose perturbation links to one single marker expression; and scMAGeCK-LR, a linear-regression based approach that unravels the perturbation effects on thousands of gene expressions, especially from cells undergo multiple perturbations.
library(scMAGeCK) ### BARCODE file contains cell identity information, generated from the cell identity collection step BARCODE <- system.file("extdata","barcode_rec.txt",package = "scMAGeCK") ### RDS can be a Seurat object or local RDS file path that contains the scRNA-seq dataset RDS <- system.file("extdata","singles_dox_mki67_v3.RDS",package = "scMAGeCK") ### Set RRA executable file path. ### You can generate RRA executable file by following commands: ### wget https://bitbucket.org/weililab/scmageck/downloads/RRA_0.5.9.zip ### unzip RRA_0.5.9.zip ### cd RRA_0.5.9 ### make RRAPATH <- "RRA_0.5.9/bin/RRA" target_gene <- "MKI67" rra_result <- scMAGeCK_RRA(BARCODE=BARCODE, RDS=RDS, GENE=target_gene, RRAPATH=RRAPATH, LABEL='dox_mki67', NEGCTRL=NULL, KEEPTMP=FALSE, PATHWAY=FALSE, SAVEPATH=NULL)
library(scMAGeCK) ### BARCODE file contains cell identity information, generated from the cell identity collection step BARCODE <- system.file("extdata","barcode_rec.txt",package = "scMAGeCK") ### RDS can be a Seurat object or local RDS file path that contains the scRNA-seq dataset RDS <- system.file("extdata","singles_dox_mki67_v3.RDS",package = "scMAGeCK") lr_result <- scMAGeCK_LR(BARCODE=BARCODE, RDS=RDS, LABEL='dox_scmageck_lr', NEGCTRL = 'NonTargetingControlGuideForHuman', PERMUTATION = 1000, SAVEPATH=NULL, LAMBDA=0.01) lr_score <- lr_result lr_score_pval <- lr_result
scMAGeCK-RRA will output the ranking and p values of each perturbed genes, using the RRA program in MAGeCK. Users familiar with the MAGeCK program may find it similar with the gene_summary output in MAGeCK.
Here is the example output of scMAGeCK-RRA:
Row.names items_in_group.low lo_value.low p.low FDR.low goodsgrna.low items_in_group.high lo_value.high p.high FDR.high goodsgrna.high TP53 271 0.11832 0.95619 1 48 271 1.014e-83 4.9975e-06 0.00015 184
Explanations of each column are below:
|Column|Content| |------|-------| |Row.names| Perturbed gene name| |items_in_group.low| The number of single-cells with each gene perturbed | |lo_value.low | The RRA score in negative selection (reducing the marker expression if this gene is perturbed). The RRA score uses a p value from rank order statistics to measure the degree of selection; the smaller score, the stronger the selection is. More information on the calculation of RRA score can be found in our original MAGeCK paper. | |p.low | The raw p-value (using permutation) of this gene in negative selection | |FDR.low | The false discovery rate of this gene in negative selection | |goodsgrna.low | The number of single-cells that passes the threshold and is considered in the RRA score calculation in negative selection| |items_in_group.high| The same as items_in_group.low: the number of single-cells with each gene perturbed) | |lo_value.high| The RRA score in positive selection (increasing the marker expression if this gene is perturbed| |p.high| The raw p-value (using permutation) of this gene in positive selection | |FDR.high| The false discovery rate of this gene in positive selection | |goodsgrna.high| The number of single-cells that passes the threshold and is considered in the RRA score calculation in positive selection|
scMAGeCK-LR will generate several files below:
|File|Description| |----|----------| |_score.txt|The score (similar with log fold change) of each perturbed gene (rows) on each marker gene (columns)| |_score.pval.txt|The associated p values of each score| |LR.RData|An R object to store scores and p values|
The format of score.txt and score.pval.txt is a simple table file with rows corresponding to perturbed genes and columns corresponding to marker genes. For example in the score.txt,
Perturbedgene APC ARID1A TP53 MKI67 APC 0.138075836476524 -0.0343441660045313 0.214449590551132 -0.150287676553705
This row records the effects of perturbing APC gene on the expressions of APC, ARID1A, TP53 and MKI67.
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