title: “adverSCarial, generate and analyze the vulnerability of scRNA-seq classifiers to adversarial attacks” shorttitle: “adverSCarial” author: Ghislain FIEVET ghislain.fievet@gmail.com package: adverSCarial abstract: > adverSCarial is an R Package designed for generating and analyzing the vulnerability of scRNA-seq classifiers to adversarial attacks. The package is versatile and provides a format for integrating any type of classifier. It offers functions for studying and generating two types of attacks, min change attack and max change attack. The min change attack involves making a small modification to the input to alter the classification. The max change attack involves making a large modification to the input without changing its classification. The package provides a comprehensive solution for evaluating the robustness of scRNA-seq classifiers against adversarial attacks. vignette: > %\VignetteIndexEntry{Vign03_adapt_classifiers} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8}
Prepare a classifier with CHETAH and scType
All classifiers needs to be formated in a certain way to be compatible with the adverSCarial package. We provide an example on a simple formating with CHETAH, and a more advanced formating with scType.
CHETAH
Here we demonstrate how to implement a classifier working with the single-gene and max-change attack, by taking the example of CHETAH a Bioconductor scRNA-seq classifier.
Load data
library(adverSCarial)
library(TENxPBMCData)
library(CHETAH)
library(scater)
library(scran)
First let’s load a train and a test dataset.
train_3k <- TENxPBMCData(dataset = "pbmc3k")
test_4k <- TENxPBMCData(dataset = "pbmc4k")
cell_types_3k <- system.file("extdata", "pbmc3k_cell_types.tsv", package="adverSCarial")
cell_types_3k <- read.table(cell_types_3k, sep="\t")
colData(train_3k)$celltypes <- cell_types_3k$cell_type
colnames(train_3k) = colData(train_3k)[['Barcode']]
colnames(test_4k) = colData(test_4k)[['Barcode']]
Then we process the test_4k to annotate and visualize the cell types.
We annotate cells with CHETAH.
input <- CHETAHclassifier(input = test_4k, ref_cells = train_3k)
input <- Classify(input = input, 0.00001)
colData(test_4k)$celltypes <- input$celltype_CHETAH
Process data.
test_4k <- logNormCounts(test_4k)
dec <- modelGeneVar(test_4k)
hvg <- getTopHVGs(dec, prop=0.1)
test_4k <- runPCA(test_4k, ncomponents=25, subset_row=hvg)
test_4k <- runUMAP(test_4k, dimred = 'PCA')
Visualize the results
plotUMAP(test_4k, colour_by="celltypes")
Adapt the classifier
CHETAH is a classifier that, when given a SingleCellExperiment object, returns a specific cell type from each cell. We need to adjust the classifier so that it can be used by adverSCarial.
Each classifier function has to be formated as follow to be used with the following functions: advSingleGene, advMaxChange, advGridMinChange, advRandWalkMinChange, maxChangeOverview, minSingleGeneOverview.
classifier = function(expr, clusters, target){
# `score` should be numeric between 0 and 1
# 1 being the highest confidance into the cell type classification.
c(
prediction="cell type",
odd=score)
}
The expr argument contrains the RNA expression values, can be a matrix, a data.frame or a SingleCellExperiment.
The list clusters consists of the cluster IDs for each cell in expr, and target is the ID of the cluster for which we want to have a classification. The function returns a vector with the classification result, and a trust indice.
This is how you can adapt CHETAH for adverSCarial.
CHETAHClassifier <- function(expr, clusters, target){
if (!exists("reference_3k")) {
reference_3k <<- train_3k
}
input <- CHETAHclassifier(input = expr, ref_cells = reference_3k)
input <- Classify(input = input, 0.01)
final_predictions = input$celltype_CHETAH[clusters == target]
ratio <- as.numeric(sort(table(final_predictions), decreasing = TRUE)[1]) /
sum(as.numeric(sort(table(final_predictions), decreasing = TRUE)))
predicted_class <- names(sort(table(final_predictions), decreasing = TRUE)[1])
if ( ratio < 0.3){
predicted_class <- "NA"
}
c(prediction=predicted_class,
odd=ratio)
}
This classifier takes as input a SingleCellExperiment object, you need to specify the argForClassif="SingleCellExperiment"
argument in adverSCarial function. If your classifier takes as input a matrix or a data.frame you can let the default
argForClassif="data.frame" argument.
You can now test CHETAH classifier with adverSCarial tools.
Let’s run a maxChangeAttack.
adv_max_change <- advMaxChange(test_4k, colData(test_4k)$celltypes, "CD14+ Mono", CHETAHClassifier, advMethod="perc99", maxSplitSize = 2000, argForClassif="SingleCellExperiment")
Let’s run this attack and verify if it is successful.
First we modify the test_4k SingleCellExperiment object on the target cluster, on the genes previously determined.
test_4k_adver <- advModifications(test_4k, adv_max_change@values, colData(test_4k)$celltypes, "CD14+ Mono", argForClassif="SingleCellExperiment")
Then we verify that classification is still DC.
rf_result <- CHETAHClassifier(test_4k_adver, colData(test_4k)$celltypes, "CD14+ Mono")
rf_result
## [1] "CD14+ Mono" "1"
scType
Here we demonstrate how to implement a classifier working with all the attacks, include the gradient-based CGD, by taking the example of scType a scRNA-seq classifier.
Ianevski, A., Giri, A.K., Aittokallio, T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat Commun, 2022;13:1246. https://doi.org/10.1038/s41467-022-28803-w
Load data
df_pbmc_test <- read.table("/home/gui/Dropbox/INSERM/jupyterlab/0033_these/data/v2/seurat_scaled_pbmc_test.txt")
expr_df <- df_pbmc_test[, -which(names(df_pbmc_test) == "y")]
clusters_df <- df_pbmc_test$y
names(clusters_df) <- rownames(df_pbmc_test)
RNA expression matrix.
expr_df[1:5,1:5]
## AL627309.1 AP006222.2 RP11.206L10.2 RP11.206L10.9
## AAACATACAACCAC-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AAACATTGATCAGC-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AAACGCACTGGTAC-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AAATGTTGCCACAA-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AACACGTGCAGAGG-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## LINC00115
## AAACATACAACCAC-1 -0.08223981
## AAACATTGATCAGC-1 -0.08223981
## AAACGCACTGGTAC-1 -0.08223981
## AAATGTTGCCACAA-1 -0.08223981
## AACACGTGCAGAGG-1 -0.08223981
Cell clusters
head(clusters_df)
## AAACATACAACCAC-1 AAACATTGATCAGC-1 AAACGCACTGGTAC-1 AAATGTTGCCACAA-1
## "Memory CD4 T" "Memory CD4 T" "Memory CD4 T" "Memory CD4 T"
## AACACGTGCAGAGG-1 AACCGCCTAGCGTT-1
## "Memory CD4 T" "Memory CD4 T"
We can find how to use scType here: https://github.com/IanevskiAleksandr/sc-type We adapt it so it returns:
- the predicted cell type
- a score of the prediction
- a matrix of the likelihood of each cell type for each cell. Cell types are rows and cells are columns.
- the list of the predicted cell type for each cell
Each classifier function has to be formated as follow to be used with the all the functions of the package, especially for advCGD.
classifier = function(expr, clusters, target){
c(
prediction="cell type",
odd=score,
typePredictions=my_matrix,
cellTypes=my_vector)
}
scType_classifier = function(expr, clusters, target){
expr = t(expr)
library(HGNChelper)
source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/sctype_score_.R")
source("https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R")
db_ = "https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/ScTypeDB_full.xlsx";
tissue = "Immune system" # e.g. Immune system,Pancreas,Liver,Eye,Kidney,Brain,Lung,Adrenal,Heart,Intestine,Muscle,Placenta,Spleen,Stomach,Thymus
# prepare gene sets
gs_list <- gene_sets_prepare(db_, tissue)
es.max = sctype_score(scRNAseqData = expr, scaled = T,
gs = gs_list$gs_positive, gs2 = gs_list$gs_negative)
if (sum(clusters == target) == 0 ){
return( c("UNDETERMINED",1))
}
cell_types <- apply(t(es.max[, clusters == target]), 1, function(x){
names(x[x == max(x)])[1]
})
table_cell_type <- table(cell_types)
str_class <- names(table_cell_type[order(table_cell_type, decreasing=T)][1])
my_odd <- mean(es.max[str_class, clusters==target])/10
resSCtype <- list(
# Cell type prediction for the cluster
prediction=str_class,
# Score of the predicted cell type
odd=my_odd,
# Score for each cell type for each cell
typePredictions=es.max,
# Cell type for each cell
cellTypes=cell_types)
return(resSCtype)
}
We check if the classifier works properly by asking him to predict the “NK” cluster.
classifier_results <- scType_classifier(expr_df, clusters_df, "NK")
classifier_results$prediction
## [1] "Natural killer cells"
The score of the prediction.
classifier_results$odd
## [1] 0.4461257
Likelihood of each cell type for each cell.
classifier_results$typePredictions[,clusters_df == "NK"][1:6,1:5]
## AAACCGTGTATGCG-1 AACGCCCTCGTACA-1 AAGATTACCTCAAG-1
## Pro-B cells -0.05520064 0.03507093 0.16081207
## Pre-B cells -0.59654036 -0.68228753 -0.44937559
## Naive B cells -0.93855041 -0.04346109 -0.26012173
## Memory B cells -1.00357418 -0.13690767 -0.34668843
## Plasma B cells -0.60566221 0.28942711 0.07276648
## Naive CD8+ T cells 0.79524385 1.51477730 1.87037543
## AAGCAAGAGGTGTT-1 ACAAATTGTTGCGA-1
## Pro-B cells -0.2828646 0.9118437
## Pre-B cells -0.9487842 0.5254085
## Naive B cells -1.2664716 1.7915419
## Memory B cells -1.3210825 1.6398264
## Plasma B cells -0.9335834 2.1244301
## Naive CD8+ T cells 0.9343186 0.2921489
Predicted cell type for each cell.
head(classifier_results$cellTypes)
## AAACCGTGTATGCG-1 AACGCCCTCGTACA-1 AAGATTACCTCAAG-1
## "Natural killer cells" "Natural killer cells" "Natural killer cells"
## AAGCAAGAGGTGTT-1 ACAAATTGTTGCGA-1 ACAGGTACTGGTGT-1
## "CD8+ NKT-like cells" "Natural killer cells" "Natural killer cells"
Let’s run a CGD attack
# Get significant genes
sign_genes <- getSignGenes(expr_df, clusters_df)
head(sign_genes$results)
## gene pval
## HLA.DRA HLA.DRA 1.004947e-70
## PRF1 PRF1 6.443834e-62
## NKG7 NKG7 1.070165e-79
## FCER1A FCER1A 2.765930e-18
## TYROBP TYROBP 8.127634e-85
## IL32 IL32 1.260937e-66
# Run the attack
result_cgd <- advCGD(expr_df, clusters_df, "NK", classifier=scType_classifier ,
genes=sign_genes$results$gene[1:100] ,alpha=2, epsilon=2)
Modified genes
result_cgd$modGenes
## [1] "NKG7" "GZMB"
Modified RNA expression matrix
result_cgd$expr[1:5,1:5]
## AL627309.1 AP006222.2 RP11.206L10.2 RP11.206L10.9
## AAACATACAACCAC-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AAACATTGATCAGC-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AAACGCACTGGTAC-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AAATGTTGCCACAA-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## AACACGTGCAGAGG-1 -0.05812316 -0.03357571 -0.04166819 -0.03364562
## LINC00115
## AAACATACAACCAC-1 -0.08223981
## AAACATTGATCAGC-1 -0.08223981
## AAACGCACTGGTAC-1 -0.08223981
## AAATGTTGCCACAA-1 -0.08223981
## AACACGTGCAGAGG-1 -0.08223981
Check the new classification of the result.
new_classifier_results <- scType_classifier(result_cgd$expr, clusters_df, "NK")
new_classifier_results$prediction
## [1] "CD8+ NKT-like cells"
sessionInfo()
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Paris
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] HGNChelper_0.8.1 scran_1.29.0
## [3] scater_1.29.0 scuttle_1.11.0
## [5] CHETAH_1.17.0 ggplot2_3.4.2
## [7] TENxPBMCData_1.19.0 HDF5Array_1.29.3
## [9] rhdf5_2.45.0 DelayedArray_0.27.5
## [11] SparseArray_1.1.10 S4Arrays_1.1.4
## [13] Matrix_1.5-4.1 SingleCellExperiment_1.23.0
## [15] SummarizedExperiment_1.31.1 Biobase_2.61.0
## [17] GenomicRanges_1.53.1 GenomeInfoDb_1.37.2
## [19] IRanges_2.35.2 S4Vectors_0.39.1
## [21] BiocGenerics_0.47.0 MatrixGenerics_1.13.0
## [23] matrixStats_1.0.0 adverSCarial_1.3.6
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.5
## [3] magrittr_2.0.3 ggbeeswarm_0.7.2
## [5] farver_2.1.1 corrplot_0.92
## [7] zlibbioc_1.47.0 vctrs_0.6.3
## [9] memoise_2.0.1 DelayedMatrixStats_1.23.0
## [11] RCurl_1.98-1.12 htmltools_0.5.5
## [13] AnnotationHub_3.9.1 curl_5.0.1
## [15] BiocNeighbors_1.19.0 Rhdf5lib_1.23.0
## [17] htmlwidgets_1.6.2 plyr_1.8.8
## [19] plotly_4.10.2 cachem_1.0.8
## [21] igraph_1.5.0 mime_0.12
## [23] lifecycle_1.0.3 pkgconfig_2.0.3
## [25] rsvd_1.0.5 R6_2.5.1
## [27] fastmap_1.1.1 GenomeInfoDbData_1.2.10
## [29] shiny_1.7.4 digest_0.6.32
## [31] colorspace_2.1-0 AnnotationDbi_1.63.1
## [33] dqrng_0.3.0 irlba_2.3.5.1
## [35] ExperimentHub_2.8.0 RSQLite_2.3.1
## [37] beachmat_2.17.8 labeling_0.4.2
## [39] filelock_1.0.2 fansi_1.0.4
## [41] httr_1.4.6 compiler_4.3.0
## [43] bit64_4.0.5 withr_2.5.0
## [45] BiocParallel_1.35.2 viridis_0.6.3
## [47] DBI_1.1.3 highr_0.10
## [49] dendextend_1.17.1 rappdirs_0.3.3
## [51] bluster_1.11.1 tools_4.3.0
## [53] vipor_0.4.5 beeswarm_0.4.0
## [55] interactiveDisplayBase_1.39.0 zip_2.3.0
## [57] httpuv_1.6.11 glue_1.6.2
## [59] rhdf5filters_1.13.3 promises_1.2.0.1
## [61] grid_4.3.0 cluster_2.1.4
## [63] reshape2_1.4.4 generics_0.1.3
## [65] gtable_0.3.3 tidyr_1.3.0
## [67] data.table_1.14.8 metapod_1.9.0
## [69] BiocSingular_1.17.0 ScaledMatrix_1.9.1
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## [81] BiocFileCache_2.9.0 lattice_0.21-8
## [83] bit_4.0.5 tidyselect_1.2.0
## [85] locfit_1.5-9.8 Biostrings_2.69.1
## [87] knitr_1.43 gridExtra_2.3
## [89] edgeR_3.43.7 xfun_0.39
## [91] statmod_1.5.0 pheatmap_1.0.12
## [93] stringi_1.7.12 lazyeval_0.2.2
## [95] yaml_2.3.7 evaluate_0.21
## [97] codetools_0.2-19 tibble_3.2.1
## [99] BiocManager_1.30.21 cli_3.6.1
## [101] uwot_0.1.15 xtable_1.8-4
## [103] munsell_0.5.0 Rcpp_1.0.10
## [105] bioDist_1.73.0 dbplyr_2.3.2
## [107] png_0.1-8 parallel_4.3.0
## [109] ellipsis_0.3.2 blob_1.2.4
## [111] sparseMatrixStats_1.13.0 bitops_1.0-7
## [113] viridisLite_0.4.2 scales_1.2.1
## [115] openxlsx_4.2.5.2 purrr_1.0.1
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