BulkRNA-Seq
Sean Wilson
2020-11-12
Last updated: 2020-12-28
Checks: 7 0
Knit directory: HowdenWilson2020/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 07adb2a | Sean Wilson | 2020-12-28 | Build site. |
| Rmd | 11477de | Sean Wilson | 2020-12-28 | Updating additional information around analysis |
| html | 1297fee | Sean Wilson | 2020-12-28 | Build site. |
| html | 84370d5 | Sean Wilson | 2020-11-13 | Build site. |
| Rmd | 711a2c5 | Sean Wilson | 2020-11-13 | Set up initial repo on github |
Introduction
We generated a number of Bulk RNA-Seq samples to analyse the transcriptomes of cell populations and cultures from this study.
In the first instance, we used a GATA3:mCherry reporter iPS cell line to generate organoids. These organoids were dissociated and stained with a conjugated EPCAM-Fluor488 antibody before being FACS sorted. We collected the various populations and Bulk RNA-Sequenced them. Ureteric Epithelium cultures generated from the GATA3+/EPCAM+ cells cultured in UE supporting conditions were also collected and sequenced.
These samples are “batch 1”.
Loading required package: limmaLoading required package: pkgmakerLoading required package: registryLoading required package: rngtoolsLoading required package: clusterNMF - BioConductor layer [OK] | Shared memory capabilities [NO: synchronicity] | Cores 31/32 To enable shared memory capabilities, try: install.extras('
NMF
')Loading required package: AnnotationDbiLoading required package: stats4Loading required package: IRangesLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following object is masked from 'package:NMF':
nrunThe following object is masked from 'package:pkgmaker':
new2The following object is masked from 'package:base':
expand.gridLoading required package: OrganismDbiLoading required package: GenomicFeaturesLoading required package: GenomeInfoDbLoading required package: GenomicRangesLoading required package: GO.dbLoading required package: org.Hs.eg.dbLoading required package: TxDb.Hsapiens.UCSC.hg19.knownGene
Attaching package: 'gplots'The following object is masked from 'package:IRanges':
spaceThe following object is masked from 'package:S4Vectors':
spaceThe following object is masked from 'package:stats':
lowessRegistered S3 method overwritten by 'cli':
method from
print.boxx spatstat── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.3.0 ✓ purrr 0.3.4
✓ tibble 3.0.4 ✓ dplyr 1.0.2
✓ tidyr 1.0.2 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.0── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::collapse() masks IRanges::collapse()
x dplyr::combine() masks Biobase::combine(), BiocGenerics::combine()
x dplyr::desc() masks IRanges::desc()
x tidyr::expand() masks S4Vectors::expand()
x dplyr::filter() masks stats::filter()
x dplyr::first() masks S4Vectors::first()
x dplyr::lag() masks stats::lag()
x ggplot2::Position() masks BiocGenerics::Position(), base::Position()
x purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
x dplyr::rename() masks S4Vectors::rename()
x dplyr::select() masks OrganismDbi::select(), AnnotationDbi::select()
x dplyr::slice() masks IRanges::slice()Preparing the data
ID Group
1 UEculture_r1 UEculture
2 UEculture_r2 UEculture
3 UEculture_r3 UEculture
4 Org_mCh_EPCAM_r1 mCh_EPCAM
5 Org_mCh_EPCAM_r2 mCh_EPCAM
6 Org_mCh_EPCAM_r3 mCh_EPCAM
7 Org_mCh_only_r1 mCh_only
8 Org_mCh_only_r2 mCh_only
9 Org_mCh_only_r3 mCh_only
10 Org_EPCAM_only_r1 EPCAM_only
11 Org_EPCAM_only_r2 EPCAM_only
12 Org_EPCAM_only_r3 EPCAM_onlyCreate a DGElist object and add gene annotation information to the data.
'select()' returned 1:many mapping between keys and columnsVisualise library sizes
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
Remove genes with less than 1 count per million in at least 3 samples. Then normalise using TMM.
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
[1] 500 12
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
Statistical analysis
The differential expression analysis is performed using voom and limma. The voom (Law et al. 2014) function is used on the normalized counts to robustly estimate the mean-variance relationship and generate precision weights for each observation. Gene-wise linear models are then fitted to the voom-transformed log2 CPM to determine differences in gene expression.
Statistically significant differentially expressed genes are identified using empirical Bayes moderated t-tests (Smyth 2005), allowing for a mean-variance trend and performing robust empirical Bayes shrinkage of the gene-wise variances to protect against hypervariable genes (Phipson, Lee, et al. 2016). P-values are adjusted for false discovery rate using the Benjamini-Hochberg method (Benjamini & Hochberg 1995).
Set up the design matrix and apply voom to the counts.
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
Fit the linear model.
Summarise the numbers of differentially expressed genes for each comparison.
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
As there are many differentially expressed genes in the original analysis, we will also test relative to a threshold (TREAT) of absolute log2 fold change > 1.
Summarise the numbers of differentially expressed genes for each comparison. There are fewer DE genes from the TREAT analysis.
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
Gene set scores
We generated from the human fetal kidney single cell data Gene Sets that we could use to identify each population within the HFK and ask how similar each sample. The Gene Sets are derived from stringent DGE tests between each individual cluster and finding sets of genes that are most representative for each cluster identity.
These are generated in the below script.
Then we can visualise how these look on a pseudobulk analysis of the original HFK data.
'select()' returned 1:many mapping between keys and columns[1] "95 N.DCT"
[1] "28 N.NPC_Primed"
[1] "165 N.NPC_CC"
[1] "68 N.NPC"
[1] "24 N.Distal_EN"
[1] "170 S.IC"
[1] "228 U.Med_Inner"
[1] "216 S.Med"
[1] "548 Endothelium"
[1] "119 N.CS"
[1] "406 N.Pod_Mat"
[1] "216 N.Pod_Dev"
[1] "47 N.Medial_EN"
[1] "134 U.Cortical"
[1] "47 N.RV"
[1] "133 S.OC_NZ"
[1] "113 N.PEC"
[1] "27 N.DST"
[1] "11 S.NP_Str"
[1] "42 N.NP_Str"
[1] "205 N.PT_Dev"
[1] "117 U.Med_Outer"
[1] "31 N.NPC_PTA"
[1] "385 N.PT_Mat"
[1] "72 U.Tip"
[1] "231 N.NPC_PTA_CC"
[1] "710 Immune"
[1] "156 N.LoH"
[1] "183 S.Mesangial"
| Version | Author | Date |
|---|---|---|
| 07adb2a | Sean Wilson | 2020-12-28 |
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] forcats_0.5.0
[2] stringr_1.4.0
[3] dplyr_1.0.2
[4] purrr_0.3.4
[5] readr_1.4.0
[6] tidyr_1.0.2
[7] tibble_3.0.4
[8] ggplot2_3.3.0
[9] tidyverse_1.3.0
[10] gplots_3.1.0
[11] Glimma_1.14.0
[12] Homo.sapiens_1.3.1
[13] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[14] org.Hs.eg.db_3.8.2
[15] GO.db_3.8.2
[16] OrganismDbi_1.28.0
[17] GenomicFeatures_1.36.4
[18] GenomicRanges_1.36.1
[19] GenomeInfoDb_1.20.0
[20] AnnotationDbi_1.46.1
[21] IRanges_2.20.2
[22] S4Vectors_0.24.3
[23] NMF_0.22.0
[24] bigmemory_4.5.36
[25] Biobase_2.46.0
[26] BiocGenerics_0.32.0
[27] cluster_2.1.0
[28] rngtools_1.5
[29] pkgmaker_0.32.2
[30] registry_0.5-1
[31] RColorBrewer_1.1-2
[32] Seurat_3.2.2
[33] edgeR_3.28.1
[34] limma_3.42.2
[35] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] reticulate_1.16 tidyselect_1.1.0
[3] RSQLite_2.2.1 htmlwidgets_1.5.1
[5] grid_3.6.1 BiocParallel_1.20.1
[7] Rtsne_0.15 munsell_0.5.0
[9] codetools_0.2-16 ica_1.0-2
[11] statmod_1.4.35 future_1.16.0
[13] miniUI_0.1.1.1 withr_2.2.0
[15] colorspace_1.4-1 knitr_1.28
[17] rstudioapi_0.11 ROCR_1.0-11
[19] tensor_1.5 listenv_0.8.0
[21] git2r_0.27.1 GenomeInfoDbData_1.2.1
[23] polyclip_1.10-0 bit64_4.0.5
[25] rprojroot_1.3-2 vctrs_0.3.4
[27] generics_0.1.0 xfun_0.12
[29] R6_2.5.0 doParallel_1.0.16
[31] rsvd_1.0.3 locfit_1.5-9.4
[33] bitops_1.0-6 spatstat.utils_1.17-0
[35] DelayedArray_0.12.2 assertthat_0.2.1
[37] promises_1.1.0 scales_1.1.1
[39] gtable_0.3.0 globals_0.13.0
[41] goftest_1.2-2 rlang_0.4.7
[43] splines_3.6.1 rtracklayer_1.44.4
[45] lazyeval_0.2.2 broom_0.7.2
[47] modelr_0.1.6 BiocManager_1.30.10
[49] yaml_2.2.1 reshape2_1.4.4
[51] abind_1.4-5 backports_1.1.10
[53] httpuv_1.5.2 RBGL_1.62.1
[55] tools_3.6.1 gridBase_0.4-7
[57] ellipsis_0.3.1 ggridges_0.5.2
[59] Rcpp_1.0.5 plyr_1.8.6
[61] progress_1.2.2 zlibbioc_1.32.0
[63] RCurl_1.98-1.2 prettyunits_1.1.1
[65] rpart_4.1-15 deldir_0.1-29
[67] pbapply_1.4-3 cowplot_1.1.0
[69] zoo_1.8-8 haven_2.2.0
[71] SummarizedExperiment_1.16.1 ggrepel_0.8.2
[73] fs_1.5.0 here_0.1
[75] magrittr_1.5 data.table_1.13.0
[77] reprex_0.3.0 lmtest_0.9-38
[79] RANN_2.6.1 whisker_0.4
[81] fitdistrplus_1.1-1 matrixStats_0.57.0
[83] hms_0.5.3 patchwork_1.0.0
[85] mime_0.9 evaluate_0.14
[87] xtable_1.8-4 XML_3.99-0.3
[89] readxl_1.3.1 gridExtra_2.3
[91] compiler_3.6.1 biomaRt_2.40.5
[93] KernSmooth_2.23-17 crayon_1.3.4
[95] htmltools_0.5.0 mgcv_1.8-33
[97] later_1.0.0 lubridate_1.7.9
[99] DBI_1.1.0 dbplyr_1.4.4
[101] MASS_7.3-53 rappdirs_0.3.1
[103] Matrix_1.2-18 cli_2.1.0
[105] igraph_1.2.5 pkgconfig_2.0.3
[107] bigmemory.sri_0.1.3 GenomicAlignments_1.20.1
[109] plotly_4.9.2.1 xml2_1.2.5
[111] foreach_1.5.1 XVector_0.24.0
[113] rvest_0.3.5 digest_0.6.25
[115] sctransform_0.3.1 RcppAnnoy_0.0.16
[117] graph_1.64.0 spatstat.data_1.4-3
[119] Biostrings_2.52.0 cellranger_1.1.0
[121] rmarkdown_2.1 leiden_0.3.3
[123] uwot_0.1.8 shiny_1.4.0.2
[125] Rsamtools_2.0.3 gtools_3.8.2
[127] lifecycle_0.2.0 nlme_3.1-150
[129] jsonlite_1.7.1 fansi_0.4.1
[131] viridisLite_0.3.0 pillar_1.4.3
[133] lattice_0.20-41 fastmap_1.0.1
[135] httr_1.4.1 survival_3.2-7
[137] glue_1.4.2 spatstat_1.63-3
[139] png_0.1-7 iterators_1.0.13
[141] bit_4.0.4 stringi_1.5.3
[143] blob_1.2.1 caTools_1.18.0
[145] memoise_1.1.0 irlba_2.3.3
[147] future.apply_1.6.0