This repository contains the code for analysis of single-cell RNA-seq data of Bagnoli et al., 2017.
Single-cell RNA sequencing (scRNA-seq) has emerged as the central genome-wide method to characterize cellular identities and processes. While performance of scRNA-seq methods is improving, an optimum in terms of sensitivity, cost-efficiency and flexibility has not yet been reached. Among the flexible plate-based methods "Single-Cell RNA-Barcoding and Sequencing" (SCRB-seq) is one of the most sensitive and efficient ones. Based on this protocol, we systematically evaluated experimental conditions such as reverse transcriptases, reaction enhancers and PCR polymerases. We find that adding polyethylene glycol considerably increases sensitivity by enhancing cDNA synthesis. Furthermore, using Terra polymerase increases efficiency due to a more even cDNA amplification that requires less sequencing of libraries. We combined these and other improvements to a new scRNA-seq library protocol we call "molecular crowding SCRB-seq" (mcSCRB-seq), which we show to be the most sensitive and one of the most efficient and flexible scRNA-seq methods to date.
All scRNA-seq data was preprocessed with zUMIs (Parekh et al., 2017).
The command was run as follows:
bash zUMIs-master.sh -f JM8.read1.fastq.gz -r JM8.read2.fastq.gz -n JM8 -g /data/ngs/genomes/Mouse/mm10/STAR5idx_ERCC_noGTF/ -a /data/ngs/genomes/Mouse/mm10/Mus_musculus.GRCm38.75.clean.spike.gtf -c 1-14 -m 15-24 -l 50 -z 2 -u 3 -p 16 -R yes -d 10000,20000,30000,40000,50000,60000,70000,80000,90000,100000,200000,300000,400000,500000,600000,700000,800000,900000,1000000,2000000,3000000,4000000,5000000This resulted in the JM8.rds object found in this repository.
> sessionInfo()
R version 3.4.0 (2017-04-21)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] de_DE.UTF-8/de_DE.UTF-8/de_DE.UTF-8/C/de_DE.UTF-8/de_DE.UTF-8
attached base packages:
[1] stats4 grid parallel splines stats graphics grDevices utils datasets methods base
other attached packages:
[1] hexbin_1.27.1 biomaRt_2.32.0 RColorBrewer_1.1-2 scales_0.5.0 scran_1.4.4
[6] BiocParallel_1.10.1 scater_1.4.0 Biobase_2.36.2 BiocGenerics_0.22.0 edgeR_3.18.1
[11] limma_3.32.2 matrixStats_0.52.2 ineq_0.2-13 Hmisc_4.0-3 Formula_1.2-1
[16] survival_2.41-3 lattice_0.20-35 bbmle_1.0.19 powsimRDev_0.0.905 doMC_1.3.4
[21] iterators_1.0.8 foreach_1.4.3 gamlss.dist_5.0-2 bindrcpp_0.2 MASS_7.3-47
[26] cowplot_0.8.0 dplyr_0.7.2 ggplot2_2.2.1
loaded via a namespace (and not attached):
[1] SparseM_1.77 rtracklayer_1.36.3 ggthemes_3.4.0 R.methodsS3_1.7.1
[5] lavaan_0.5-23.1097 coda_0.19-1 nonnest2_0.4-1 tidyr_0.7.0
[9] acepack_1.4.1 bit64_0.9-7 knitr_1.16 irlba_2.2.1
[13] aroma.light_3.6.0 DelayedArray_0.2.7 R.utils_2.5.0 Rook_1.1-1
[17] data.table_1.10.5 rpart_4.1-11 hwriter_1.3.2 RCurl_1.95-4.8
[21] doParallel_1.0.10 snow_0.4-2 GenomicFeatures_1.28.2 RSQLite_2.0
[25] VGAM_1.0-4 combinat_0.0-8 bit_1.1-12 httpuv_1.3.5
[29] ggsci_2.7 SummarizedExperiment_1.6.3 DrImpute_1.0 assertthat_0.2.0
[33] viridis_0.4.0 tximport_1.4.0 RMTstat_0.3 IHW_1.4.0
[37] caTools_1.17.1 igraph_1.0.1 DBI_0.7 geneplotter_1.54.0
[41] htmlwidgets_0.8 EDASeq_2.10.0 RcppArmadillo_0.7.960.1.2 purrr_0.2.3
[45] backports_1.1.0 DDRTree_0.1.5 pbivnorm_0.6.0 permute_0.9-4
[49] scDD_1.0.0 annotate_1.54.0 moments_0.14 RcppParallel_4.3.20
[53] blockmodeling_0.1.8 Cairo_1.5-9 quantreg_5.33 abind_1.4-5
[57] withr_1.0.2 RcppEigen_0.3.3.3.0 checkmate_1.8.2 GenomicAlignments_1.12.1
[61] fdrtool_1.2.15 mclust_5.3 SCnorm_0.99.7 mnormt_1.5-5
[65] cluster_2.0.6 DEDS_1.50.0 NBPSeq_0.3.0 lazyeval_0.2.0
[69] crayon_1.3.2 genefilter_1.58.1 glmnet_2.0-10 pkgconfig_2.0.1
[73] slam_0.1-40 labeling_0.3 GenomeInfoDb_1.12.1 nlme_3.1-131
[77] vipor_0.4.5 devtools_1.13.2 nnet_7.3-12 bindr_0.1
[81] rlang_0.1.2 miniUI_0.1.1 MatrixModels_0.4-1 sandwich_2.3-4
[85] extRemes_2.0-8 BPSC_0.99.1 cidr_0.1.5 distillery_1.0-2
[89] Matrix_1.2-9 BASiCS_0.7.30 lpsymphony_1.4.1 zoo_1.8-0
[93] base64enc_0.1-3 beeswarm_0.2.3 pheatmap_1.0.8 viridisLite_0.2.0
[97] rjson_0.2.15 bitops_1.0-6 shinydashboard_0.6.0 NOISeq_2.20.0
[101] R.oo_1.21.0 Lmoments_1.2-3 spam_1.4-0 KernSmooth_2.23-15
[105] ggExtra_0.7 Biostrings_2.44.1 EBSeq_1.16.0 blob_1.1.0
[109] rgl_0.98.1 stringr_1.2.0 qvalue_2.8.0 msir_1.3.1
[113] brew_1.0-6 arm_1.9-3 ShortRead_1.34.0 NbClust_3.0
[117] S4Vectors_0.14.3 memoise_1.1.0 magrittr_1.5 plyr_1.8.4
[121] gplots_3.0.1 gdata_2.18.0 zlibbioc_1.22.0 compiler_3.4.0
[125] HSMMSingleCell_0.110.0 pcaMethods_1.68.0 lme4_1.1-13 DESeq2_1.16.1
[129] fitdistrplus_1.0-9 Rsamtools_1.28.0 ade4_1.7-8 DSS_2.16.0
[133] XVector_0.16.0 htmlTable_1.9 mgcv_1.8-17 ROTS_1.4.0
[137] MAST_1.2.1 stringi_1.1.5 densityClust_0.2.1 locfit_1.5-9.1
[141] latticeExtra_0.6-28 tools_3.4.0 monocle_2.4.0 foreign_0.8-67
[145] outliers_0.14 bsseq_1.12.1 gridExtra_2.2.1 Rtsne_0.13
[149] digest_0.6.12 FNN_1.1 shiny_1.0.5 qlcMatrix_0.9.5
[153] quadprog_1.5-5 Rcpp_0.12.12 car_2.1-4 GenomicRanges_1.28.3
[157] pscl_1.4.9 AnnotationDbi_1.38.2 minpack.lm_1.2-1 colorspace_1.3-2
[161] XML_3.98-1.7 fields_9.0 IRanges_2.10.2 statmod_1.4.29
[165] flexmix_2.3-14 xtable_1.8-2 nloptr_1.0.4 jsonlite_1.5
[169] baySeq_2.10.0 dynamicTreeCut_1.63-1 modeltools_0.2-21 testthat_1.0.2
[173] R6_2.2.2 clusterCrit_1.2.7 htmltools_0.3.6 mime_0.5
[177] minqa_1.2.4 glue_1.1.1 DT_0.2 DESeq_1.28.0
[181] RUVSeq_1.10.0 codetools_0.2-15 maps_3.1.1 mvtnorm_1.0-6
[185] tibble_1.3.4 pbkrtest_0.4-7 numDeriv_2016.8-1 ggbeeswarm_0.5.3
[189] scde_1.99.4 gtools_3.5.0 openxlsx_4.0.17 CompQuadForm_1.4.3
[193] cobs_1.3-3 fastICA_1.2-0 munsell_0.4.3 rhdf5_2.20.0
[197] GenomeInfoDbData_0.99.0 reshape2_1.4.2 gtable_0.2.0 NBGOF_0.2.2