scDist: Robust identification of perturbed cell types in single-cell RNA-seq data

R package version 1.1.4

System Requirements

R is required to use scDist. In development, R version 4.0.0 and greater were used, but there may be compatibility with previous versions.

Installation

From the R console, devtools::install_github("phillipnicol/scDist"). Installation should take less than a minute on a standard machine.

Demo

The input to scDist is a normalized count matrix and correpsonding metadata that describes what condition and patient each cell belongs to. In this demo, we create a simulated dataset with 10 cell types. The demo should take less than a minute to run on a standard machine. The code is also modifiable to see how scDist performs for different parameter values.

library(scDist)
set.seed(1126490984)

Generate simulated data with 10 cell types and 5 patients in each group:

sim <- simData(nct=10,N1=5,N2=5)

dim(sim$Y) #Normalized counts

## [1] 1000 5100

rownames(sim$Y) <- 1:1000
head(sim$meta.data)

##   response patient clusters
## 1        1       1        a
## 2        1       1        a
## 3        1       1        a
## 4        1       1        a
## 5        1       1        a
## 6        1       1        a

Now we apply scDist:

out <- scDist(sim$Y,sim$meta.data,fixed.effects = "response",
              random.effects="patient",
              clusters="clusters")

## ================================================================================

The results data frame gives a summary of the estimated distance and uncertainty for each cell type

out$results

##       Dist. 95% CI (low) 95% CI (upper)        p.val
## a 33.044137    32.281250       33.82516 0.0000099999
## b 10.870614     8.007240       13.73925 0.0048399516
## c  9.885715     5.217373       13.39083 0.4479655203
## d 15.240428    13.991557       16.58055 0.0000099999
## e 18.897089    18.201092       19.64802 0.0000099999
## f  0.000000     0.000000        0.00000 0.9478205218
## g 33.022750    32.519064       33.55178 0.0000099999
## h 22.591648    21.749534       23.49203 0.0000099999
## i 16.613041    15.731826       17.54432 0.0000099999
## j 16.371930    15.131200       17.60823 0.0000099999

The true distances are

names(sim$D.true) <- letters[1:length(sim$D.true)]
sim$D.true

##         a         b         c         d         e         f         g         h 
## 31.497609  4.408609  5.153658 10.700209 15.551271  2.764961 31.310492 19.893417 
##         i         j 
## 13.349565 13.045934

We can also plot the results

DistPlot(out)

To get a plot of genes that are associated with the perturbation use distGenes:

distGenes(out, cluster = "a")

Reference

If you use scDist in your work, please cite:

Nicol, P.B., Paulson, D., Qian, G., Liu, X.S., Irizarry, R.A., and Sahu, A.D. (2024). Robust identification of perturbed cell types in single-cell RNA-seq data. Nature Communications. Vol 15(7610). https://doi.org/10.1038/s41467-024-51649-3.