stela2502 / drimpute

Imputing dropout events in single-cell RNA-sequencing data

Fork from the original DrImpute on Github.

The original code is not usable in R 3.6.0. I tried to use this modified one here which at least exports the preprocess function (!). Using my own data and own grouping I could if I load the module via devtools::document() start the 'imp0clC' function directly and obtain imputed data, but the function is still not exported if you load the package vir library().

In ist center the imputed data is what you brain can/could do, too. If you see that in one group you have quite some genes expressed, but on a low level and with many dropouts - just assume you have a constant low expression in this group. The tool does also not change already detected expression values.

All in all nothing I would use at the moment. But I have only invested half a day into trying. The level of R code I got here did not convince me either. I plann to check in more detail later and update this quite harsh beakdown of DrImpute on R code level. Maybe I even get it to work for me ;-)

DrImpute: Imputing dropout events in single cell RNA sequencing data

Wuming Gong ([email protected]) and Il-Youp Kwak ([email protected]).

R/DrImpute is an R package for imputing dropout events in single-cell RNA-sequencing data. It improve many statistical tools used for scRNA-seq analysis that do not account for dropout events.

The paper can be found https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2226-y.

Wuming Gong†, Il-Youp Kwak†, Pruthvi Pota, Naoko Koyano-Nakagawa and Daniel J. Garry
BMC Bioinformatics, 2018, 19:220

1. Installation

The recommended installation method for DrImpute is using install_github command from devtools library. You will first have to have devtools package installed.

library(devtools)
install_github('gongx030/DrImpute')

A number of needed packages are installed in this process.

2. Quick Start

We first load the tcm package:

library(DrImpute)

We load the scRNA-seq dataset on mouse sensory neurons that have 622 cells from four major types of cells: NP, TH, NF and PEP.

install_github('gongx030/scDatasets')
library(scDatasets)
library(SummarizedExperiment)
data(usoskin)
usoskin

We extract the read count matrix and the cell labels. The the genes that are expressed in zero or only one cell will be removed by the function preprocessSC.

X <- assays(usoskin)$count
X <- preprocess(X, min.expressed.gene = 0)
X.log <- log(X + 1)
class.label <- colData(usoskin)[['Level.1']]

Then we run the imputation with the DrImpute:

set.seed(1)
X.imp <- DrImpute(X.log)

We can compare the clustering performance (t-SNE followed by k-means) by using the scRNA datasets before and after the imputation:

library(mclust)
library(Rtsne)
# before imputation
adjustedRandIndex(kmeans(Rtsne(t(as.matrix(X.log)))$Y, centers = 4)$cluster, class.label)
# after imputation
adjustedRandIndex(kmeans(Rtsne(t(as.matrix(X.imp)))$Y, centers = 4)$cluster, class.label)

3. Session Information

> sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.9 (Final)

Matrix products: default
BLAS: /panfs/roc/msisoft/R/3.4.3/lib64/R/lib/libRblas.so
LAPACK: /panfs/roc/msisoft/R/3.4.3/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices datasets  utils     methods   base

other attached packages:
 [1] Rtsne_0.13                 mclust_5.4                 scDatasets_0.0.3           SummarizedExperiment_1.8.1 DelayedArray_0.4.1         matrixStats_0.53.1         Biobase_2.38.0             GenomicRanges_1.30.3       GenomeInfoDb_1.14.0        IRanges_2.12.0             S4Vectors_0.16.0           BiocGenerics_0.24.0
[13] DrImpute_1.1               BiocInstaller_1.28.0

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17           XVector_0.18.0         xml2_1.2.0             magrittr_1.5           roxygen2_6.0.1         zlibbioc_1.24.0        devtools_1.13.5        lattice_0.20-35        R6_2.2.2               FNN_1.1                stringr_1.3.0          tools_3.4.3            grid_3.4.3             irlba_2.3.2
[15] withr_2.1.2            commonmark_1.4         digest_0.6.15          Matrix_1.2-12          GenomeInfoDbData_1.0.0 bitops_1.0-6           RCurl_1.95-4.10        memoise_1.1.0          stringi_1.1.7          compiler_3.4.3