Last updated: 2025-10-18
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Knit directory: Canon-analysis/
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In this tutorial, we illustrate the use of Canon through one subset sc-CRISPR dataset which can be downloaded here: KRAS cancer data
In this step, we load the data into the environment
load("~/Projects/MR_perturbation/tutorial_example_data.rds")
gene_expression <- data_example$gene_expression
gRNA_mat <- data_example$gRNALet’s take a brief look at the gene expression data and gRNA data respectively.
print(gene_expression[1:5, 1:5]) ATCACGATCGATAGAA-1-31 ACTTGTTAGTTGTAGA-1-27 TTGGAACTCGGAAACG-1-14
SAMD11 -0.3943890 2.6997235 -0.4345025
HES4 -0.9122419 -0.8447953 -0.7541952
ISG15 -0.4786171 -0.3884422 -0.3706382
ERRFI1 -0.5157255 0.6373354 -0.8968230
ENO1 1.0510503 -1.5117935 -0.3409592
ACATCAGTCCCTAATT-1-31 CTAAGACGTGTGCCTG-1-19
SAMD11 -0.4461569 -0.4918990
HES4 -0.5702732 -0.6156242
ISG15 2.9862552 -0.3358234
ERRFI1 -0.7713618 0.7877674
ENO1 0.6426609 0.7882333print(gRNA_mat[1:5, 1:5]) T20R T158T M111L V112I T50I
ATCACGATCGATAGAA-1-31 0 0 0 0 0
ACTTGTTAGTTGTAGA-1-27 0 0 0 0 0
TTGGAACTCGGAAACG-1-14 0 0 0 0 0
ACATCAGTCCCTAATT-1-31 0 0 1 0 0
CTAAGACGTGTGCCTG-1-19 0 0 0 0 0The core function of Canon is run_Canon. For a detailed
description of all the input parameters and output values, please refer
to its documentation by typing ?run_Canon in R. Canon is
able to model an initial set of candidate gRNAs and perform automated
instrument selection to identify suitable gRNAs to serve as instrumental
variables. Additionally, Canon relies on a scalable sampling-based
inference algorithm to identify genes that are potentially causally
influenced by perturbed target genes across diverse sc-CRISPR
platforms.
We use gene PLAU as an example to illustrate the usage of Canon
library(Canon)
xin <- scale(as.matrix(gene_expression["KRAS", ]))
yin <- scale(as.matrix(gene_expression["PLAU", ]))
zin <- scale(gRNA_mat)
## The prior information can be adjusted based on the experience
cond <- try(result <- run_Canon(x_expression = xin, y_expression = yin, gRNA = zin,
Gibbsnumber = 1000, pi_beta_shape = 0.7, pi_beta_scale = 0.7,
a_beta = 21, b_beta = 0.01, pi_eta_shape = 1, pi_eta_scale = 1,
off_target = T))## Causal effect estimate
print(result$causal_effect)[1] 0.5347347## Causal effect pvalue
print(result$causal_pvalue)[1] 0.01260536
sessionInfo()R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Canon_1.0 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] jsonlite_1.8.8 compiler_4.5.1 promises_1.3.0 Rcpp_1.0.12
[5] stringr_1.5.1 git2r_0.36.2 callr_3.7.6 later_1.4.4
[9] jquerylib_0.1.4 yaml_2.3.8 fastmap_1.2.0 R6_2.6.1
[13] knitr_1.46 tibble_3.2.1 rprojroot_2.1.1 bslib_0.7.0
[17] pillar_1.9.0 rlang_1.1.3 utf8_1.2.4 cachem_1.1.0
[21] stringi_1.8.4 httpuv_1.6.15 xfun_0.44 getPass_0.2-4
[25] fs_1.6.4 sass_0.4.9 cli_3.6.2 magrittr_2.0.3
[29] ps_1.7.6 digest_0.6.35 processx_3.8.4 rstudioapi_0.16.0
[33] lifecycle_1.0.4 vctrs_0.6.5 evaluate_0.23 glue_1.8.0
[37] whisker_0.4.1 fansi_1.0.6 rmarkdown_2.27 httr_1.4.7
[41] tools_4.5.1 pkgconfig_2.0.3 htmltools_0.5.8.1