scPOP

scPOP is a lightweight, low dependency R package which brings together multiple metrics to evaluate batch correction for single cell RNA-seq. The package includes the Local Simpson Index (LISI) and Average Silhouette Width (ASW) metrics from Harmony and kBET, respectively, as well as the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI) algorithms.

Installation

Install with the following:

library(devtools)
devtools::install_github('vinay-swamy/scPOP')

Note that to install this package, you may require additional software to compile Rcpp code:

• xcode CLI for MacOS
• Rtools for Windows

Metrics

The metrics we include are :

• Adjusted Rand Index(ARI): The amount of overlap between two sets of labels.
• Normalized Mutual Information: The amount of overlap between two sets of labels.
• Silhouette Width: Average difference between within label distance and nearest label distance. This requires calculation of distance matrix, which does not scale well with larger samplesizes. We provide methods for easily subsampling data within scPOP
• Local Inverse Simpson Index (LISI): (avg) Number of labels present in the N nearest cells

It’s important to note that based on the type of label being evaluated, the “optimal” score a given metric may change. For example, when calculating LISI based on batch, a highscore is better(multiple batches close together), but when calculating based on Cell Type, a low score is better( the same celltypes are close together.)

Usage

The ideal use case for scPOP to generate metrics on dataset for which multiple rounds of batch correction have been calculated. These metrics can be used to rank different

We provide a toy dataset in .h5ad format

download.file('https://hpc.nih.gov/~mcgaugheyd/scEiaD/colab/scEiaD_all_anndata_mini_ref.h5ad', 'scEiaD_all_anndata_mini_ref.h5ad')

We recommend calculating all metrics at once using run_all_metrics. This function requires a matrix of reduced dimensions, a data.frame containing metadata, and the names of 3 columns

• batch_key: column corresponds to batch for each cell
• label1_key: primary label for each cell, ie Cell Type
• secondary_label: for each cell, ie Cluster

We recommend the zellkonverter for reading .h5ad formatted into R. The example we provide uses data in the SingleCellExperiment format, but as scPOP only requires vectors of labels and matrices of reduced dimensions, data from other frameworks like Seurat can be easily used.

library(zellkonverter,quietly = T)
library(SingleCellExperiment,quietly = T)
library(scPOP)
sce <- zellkonverter::readH5AD('scEiaD_all_anndata_mini_ref.h5ad')
sce

## class: SingleCellExperiment 
## dim: 15114 27350 
## metadata(0):
## assays(1): X
## rownames(15114): ENSG00000000005 ENSG00000000419 ... ENSG00000288602
##   ENSG00000288642
## rowData names(5): vst.mean vst.variance vst.variance.expected
##   vst.variance.standardized vst.variable
## colnames(27350): CTTTGCGAGATGTGGC_ERS2852885
##   GATCGTATCGAGAGCA_ERS2852885 ... ACGATACCAAGCTGTT_SRS6424737
##   AACTCAGAGCCCAGCT_SRS6424747
## colData names(32): nCount_RNA nFeature_RNA ... doublet_score_scran
##   CellType_predict
## reducedDimNames(2): X_scvi X_scviumap
## altExpNames(0):

Running scPOP

metrics <-run_all_metrics(reduction = reducedDim(sce, 'X_scvi'), 
                          metadata = colData(sce),
                          batch_key = 'batch',
                          label1_key = 'CellType_predict',
                          label2_key = 'cluster', 
                          run_name = 'example')

## Calculating LISI...

## Done
## Calculating Silhoette width...

## Done
## Calculating ARI...

## Done
## Calculating NMI...

## Done

metrics

##       run ari_label nmi_label lisi_batch lisi_CellType_predict lisi_cluster
## 1 example 0.4459971 0.6276339   2.400331              1.137047     1.205916
##   silWidth_batch silWidth_CellType_predict silWidth_cluster
## 1     -0.1615347                 0.2745293         0.138743

These metrics can be applied to multiple integration runs to determine the optimal integration method/parameters. To illustrate this, we’ll generate some fake data.

multi_run_example <-  lapply(c(23232, 23423423, 66774, 2341345, 56733), function(i){
    set.seed(i)
    sce_shuffled <- sce
    sce_shuffled$batch <- sample(sce_shuffled$batch, ncol(sce))
    sce_shuffled$CellType_predict <- sample(sce_shuffled$CellType_predict, ncol(sce))
    sce_shuffled$cluster <- sample(sce_shuffled$cluster, ncol(sce))
    run_all_metrics(reduction = reducedDim(sce_shuffled, 'X_scvi'), 
                  metadata = colData(sce_shuffled),
                  batch_key = 'batch',
                  label1_key = 'CellType_predict',
                  label2_key = 'cluster', 
                  run_name = as.character(i), 
                  sil_width_prop = .25, 
                  sil_width_group_key = 'CellType_predict', 
                  quietly=T)
    
})

run_metrics <-  do.call(rbind,  multi_run_example)
run_metrics

##        run     ari_label   nmi_label lisi_batch lisi_CellType_predict
## 1    23232 -5.892982e-04 0.007564143   8.174771              4.797056
## 2 23423423 -3.130038e-04 0.007987631   8.174047              4.806773
## 3    66774  9.051521e-06 0.007990235   8.210295              4.819635
## 4  2341345 -1.652940e-04 0.008120943   8.187121              4.801828
## 5    56733 -6.915233e-05 0.007969419   8.178482              4.804363
##   lisi_cluster silWidth_batch silWidth_CellType_predict silWidth_cluster
## 1     7.933616    -0.18476700                -0.2956764       -0.3619559
## 2     7.925464    -0.16575566                -0.3013349       -0.2507972
## 3     7.972977    -0.06813073                -0.2794385       -0.4588756
## 4     7.945181    -0.13626901                -0.2239636       -0.3859973
## 5     8.006362    -0.17093947                -0.2862585       -0.4382246

We provide a method, calc_sumZscore, to aggregate these metrics together across multiple runs to generate a single score for each run

run_metrics$sumZscore <-  calc_sumZscore(run_metrics, 'batch')
run_metrics[,c('run', 'sumZscore')]

##        run sumZscore
## 1    23232 -1.711125
## 2 23423423  1.283290
## 3    66774 -1.835191
## 4  2341345  3.599619
## 5    56733 -1.336593

We also provide functions for running each function individually

ari_score <- ari(sce$batch, sce$CellType_predict)
ari_score

## [1] 0.1371779

nmi_score <- nmi(sce$batch, sce$CellType_predict)
nmi_score

## [1] 0.3246491

Lisi requires about 10gb of memory for 100K cells and scales linearly with number of cells

lisi_score <- lisi(X = reducedDim(sce, 'X_scvi'), meta_data = as.data.frame(colData(sce)), label_colnames = 'CellType_predict' )
head(lisi_score)

##                             CellType_predict
## CTTTGCGAGATGTGGC_ERS2852885         1.000000
## GATCGTATCGAGAGCA_ERS2852885         1.000000
## CCTCTGACACCCAGTG_ERS2852885         1.003521
## GCGCAACAGAAGCCCA_ERS2852885         1.000000
## GACGTTATCGGTCCGA_ERS2852885         1.000000
## TGCGCAGGTACCAGTT_ERS2852885         1.000000

For some the silhouette_width, a distance matrix mist be calculated, which requires significant memory usage. We provide the function stratified_sample to downsample data based on a grouping variable

idx <- stratified_sample(colnames(sce), sce$batch)
sce_ds <- sce[,idx]
sil_score <- silhouette_width(reduction = reducedDim(sce_ds, 'X_scvi'), 
                              meta.data = colData(sce_ds),  
                              keys ='CellType_predict')