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.
## Registered S3 method overwritten by 'spatstat.core':
## method from
## formula.glmmPQL MASS ### 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 <- "/Library/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)## Checking RRA...## Warning in system("RRA", ignore.stdout = TRUE, ignore.stderr = TRUE): error in
## running command## RRA does not exist! Please check RRA executable file path 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)## run_signature: FALSE## Total barcode records: 8425## Neg Ctrl guide: NonTargetingControlGuideForHuman## Reading RDS file: /tmp/RtmpHSEP1h/Rinste79982c67d1dd/scMAGeCK/extdata/singles_dox_mki67_v3.RDS## Cell names in expression matrix and barcode file do not match. Try to remove possible trailing "-1"s...## 6704 ...## 6229 ...## Index matrix dimension: 5698 , 30## Filter genes whose expression is greater than 0 in raw read count in less than 0.01 single-cell populations.## Selected genes: 25## Selected cells: 5698## Permutation: 100 / 1000 ...## Permutation: 200 / 1000 ...## Permutation: 300 / 1000 ...## Permutation: 400 / 1000 ...## Permutation: 500 / 1000 ...## Permutation: 600 / 1000 ...## Permutation: 700 / 1000 ...## Permutation: 800 / 1000 ...## Permutation: 900 / 1000 ...## Permutation: 1000 / 1000 ...The scmageck_rra function 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 184Explanations 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 |
The scmageck_lr function will generate several files below:
| File | Description |
|---|---|
| lr_score | The score (similar with log fold change) of each perturbed gene (rows) on each marker gene (columns) |
| lr_score.pval | 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 -0.150287676553705This row records the effects of perturbing APC gene on the expressions of APC, ARID1A, TP53 and MKI67.
Questions? Comments? Join the MAGeCK Google group or email us (wli2@childrensnational.org) directly.
Any advice and suggestions will be greatly appreciated.