This package contains a collection of three publicly available single-cell RNA-seq datasets. The data were downloaded from NCBI’s SRA or from EBI’s ArrayExpress (see below for Accession numbers)
The dataset fluidigm contains 65 cells from (Pollen et al. 2014), each sequenced at high and low coverage (SRA: SRP041736).
The dataset th2 contains 96 T helper cells from (Mahata et al. 2014) (ArrayExpress: E-MTAB-2512).
The dataset allen contains 379 cells from the mouse visual cortex. This is a subset of the data published in (Tasic et al. 2016) (SRA: SRP061902).
SRA files were downloaded from the Sequence Read Archive and the SRA Toolkit was used to transform them to FASTQ. FASTQ files were downloaded from EMBL-EBI ArrayExpress.
Reads were aligned with TopHat (v. 2.0.11) (Trapnell, Pachter, and Salzberg 2009) to the appropriate reference genome (GRCh38 for human samples, GRCm38 for mouse). RefSeq mouse gene annotation (GCF_000001635.23_GRCm38.p3) was downloaded from NCBI on Dec. 28, 2014. RefSeq human gene annotation (GCF_000001405.28) was downloaded from NCBI on Jun. 22, 2015.
featureCounts (v. 1.4.6-p3) (Liao, Smyth, and Shi 2013) was used to compute gene-level read counts and Cufflinks (v. 2.2.0) (Trapnell et al. 2010) was used to compute gene-leve FPKM’s.
In parallel, reads were mapped to the transcriptome using RSEM (v. xx) (B. Li and Dewey 2011) to compute read counts and TPM’s.
FastQC (v. 0.10.1) (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and Picard (v. 1.128) (http://broadinstitute.github.io/picard) were used to compute sample quality control (QC) metrics. (Picard’s scripts CollectRnaSeqMetrics, CollectAlignmentSummaryMetrics and CollectInsertSizeMetrics).
Note that all the samples available in GEO and/or ArrayExpressed were included in the data object, including the samples that were excluded in the original publication because they did not pass QC.
The package contains each dataset in the form of SummarizedExperiment objects. To illustrate the format of each dataset, we will use the fluidigm data.
library(scRNAseq)
data(fluidigm)
fluidigm## class: SummarizedExperiment
## dim: 26255 130
## metadata(3): sample_info clusters which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(26255): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(130): SRR1275356 SRR1274090 ... SRR1275366 SRR1275261
## colData names(28): NREADS NALIGNED ... Cluster1 Cluster2We can retrieve the gene expression measures by using the assay contstruct.
head(assay(fluidigm)[,1:3])## SRR1275356 SRR1274090 SRR1275251
## A1BG 0 0 0
## A1BG-AS1 0 0 0
## A1CF 0 0 0
## A2M 0 0 0
## A2M-AS1 0 0 0
## A2ML1 0 0 0assay will return the gene-level read counts. If we want to access the FPKM values, we need the following
head(assay(fluidigm, 2)[,1:3])## SRR1275356 SRR1274090 SRR1275251
## A1BG 0 0.0000000 0
## A1BG-AS1 0 0.3256690 0
## A1CF 0 0.0687904 0
## A2M 0 0.0000000 0
## A2M-AS1 0 0.0000000 0
## A2ML1 0 1.3115300 0Alternatively, we can use the assays accessor to get a list with both matrices.
names(assays(fluidigm))## [1] "tophat_counts" "cufflinks_fpkm" "rsem_counts" "rsem_tpm"head(assays(fluidigm)$fpkm[,1:3])## NULLNote that the all the protein-coding genes are included in the expression matrix, even if they are not expressed in these samples, hence filtering of the non-expressed genes should be performed before any statistical analysis.
dim(fluidigm)## [1] 26255 130table(rowSums(assay(fluidigm))>0)##
## FALSE TRUE
## 9170 17085In addition to the gene expression levels, the object contains some useful QC information, as well as the available annotation of the samples. This information can be accessed through the colData accessor.
colData(fluidigm)## DataFrame with 130 rows and 28 columns
## NREADS NALIGNED RALIGN TOTAL_DUP PRIMER INSERT_SZ
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## SRR1275356 10554900 7555880 71.5862 58.4931 0.0217638 208
## SRR1274090 196162 182494 93.0323 14.5122 0.0366826 247
## SRR1275251 8524470 5858130 68.7213 65.0428 0.0351827 230
## SRR1275287 7229920 5891540 81.4884 49.7609 0.0208685 222
## SRR1275364 5403640 4482910 82.9609 66.5788 0.0298284 228
## ... ... ... ... ... ... ...
## SRR1275259 5949930 4181040 70.2705 52.5975 0.0205253 224
## SRR1275253 10319900 7458710 72.2747 54.9637 0.0205342 207
## SRR1275285 5300270 4276650 80.6873 41.6394 0.0227383 222
## SRR1275366 7701320 6373600 82.7600 68.9431 0.0266275 233
## SRR1275261 13425000 9554960 71.1727 62.0001 0.0200522 241
## INSERT_SZ_STD COMPLEXITY NDUPR PCT_RIBOSOMAL_BASES
## <numeric> <numeric> <numeric> <numeric>
## SRR1275356 63 0.868928 0.343113 2e-06
## SRR1274090 133 0.997655 0.935730 0e+00
## SRR1275251 89 0.789252 0.201082 0e+00
## SRR1275287 78 0.898100 0.538191 0e+00
## SRR1275364 76 0.890693 0.391660 0e+00
## ... ... ... ... ...
## SRR1275259 80 0.898898 0.399189 5e-06
## SRR1275253 62 0.863618 0.344744 0e+00
## SRR1275285 76 0.920068 0.638765 2e-06
## SRR1275366 83 0.860359 0.343122 0e+00
## SRR1275261 105 0.806833 0.234551 0e+00
## PCT_CODING_BASES PCT_UTR_BASES PCT_INTRONIC_BASES
## <numeric> <numeric> <numeric>
## SRR1275356 0.125806 0.180954 0.613229
## SRR1274090 0.309822 0.412917 0.205185
## SRR1275251 0.398461 0.473884 0.039886
## SRR1275287 0.196420 0.227592 0.498944
## SRR1275364 0.138617 0.210406 0.543941
## ... ... ... ...
## SRR1275259 0.261384 0.383665 0.264250
## SRR1275253 0.110732 0.190036 0.606814
## SRR1275285 0.143667 0.231103 0.540070
## SRR1275366 0.215696 0.307817 0.409437
## SRR1275261 0.408881 0.391068 0.147748
## PCT_INTERGENIC_BASES PCT_MRNA_BASES MEDIAN_CV_COVERAGE
## <numeric> <numeric> <numeric>
## SRR1275356 0.080008 0.306760 1.495770
## SRR1274090 0.072076 0.722739 1.007580
## SRR1275251 0.087770 0.872345 1.242990
## SRR1275287 0.077044 0.424013 0.775981
## SRR1275364 0.107035 0.349024 1.441370
## ... ... ... ...
## SRR1275259 0.090696 0.645049 1.101040
## SRR1275253 0.092418 0.300768 1.701690
## SRR1275285 0.085158 0.374770 0.714087
## SRR1275366 0.067050 0.523513 1.251980
## SRR1275261 0.052302 0.799949 0.939066
## MEDIAN_5PRIME_BIAS MEDIAN_3PRIME_BIAS
## <numeric> <numeric>
## SRR1275356 0.000000 0.166122
## SRR1274090 0.181742 0.698991
## SRR1275251 0.000000 0.340046
## SRR1275287 0.010251 0.350915
## SRR1275364 0.000000 0.204074
## ... ... ...
## SRR1275259 0.000000 0.315550
## SRR1275253 0.000000 0.106902
## SRR1275285 0.019578 0.419987
## SRR1275366 0.000000 0.281554
## SRR1275261 0.000292 0.290117
## MEDIAN_5PRIME_TO_3PRIME_BIAS sample_id.x Lane_ID
## <numeric> <character> <character>
## SRR1275356 1.036250 SRX534610 D24VYACXX130502:4
## SRR1274090 0.293510 SRX534823 1
## SRR1275251 0.201518 SRX534623 D24VYACXX130502:4
## SRR1275287 0.292838 SRX534641 D24VYACXX130502:1
## SRR1275364 0.619863 SRX534614 D24VYACXX130502:7
## ... ... ... ...
## SRR1275259 0.350391 SRX534627 D24VYACXX130502:4
## SRR1275253 0.944856 SRX534624 D24VYACXX130502:3
## SRR1275285 0.194939 SRX534640 D24VYACXX130502:1
## SRR1275366 0.388272 SRX534615 D24VYACXX130502:8
## SRR1275261 0.384402 SRX534628 D24VYACXX130502:3
## LibraryName avgLength spots Biological_Condition
## <character> <integer> <integer> <character>
## SRR1275356 GW16_2 202 9818076 GW16
## SRR1274090 NPC_9 60 95454 NPC
## SRR1275251 GW16_8 202 7935952 GW16
## SRR1275287 GW21+3_2 202 6531944 GW21+3
## SRR1275364 GW16_23 202 4919561 GW16
## ... ... ... ... ...
## SRR1275259 GW21_3 202 5528916 GW21
## SRR1275253 GW16_9 202 9562204 GW16
## SRR1275285 GW21+3_16 202 4860721 GW21+3
## SRR1275366 GW16_24 202 7153688 GW16
## SRR1275261 GW21_4 202 12142387 GW21
## Coverage_Type Cluster1 Cluster2
## <character> <factor> <factor>
## SRR1275356 High IIIb III
## SRR1274090 Low 1a I
## SRR1275251 High NA III
## SRR1275287 High 1c I
## SRR1275364 High IIIb III
## ... ... ... ...
## SRR1275259 High NA III
## SRR1275253 High IIIb III
## SRR1275285 High Iva IV
## SRR1275366 High NA III
## SRR1275261 High II IIThe first columns are related to sample quality, while other fields include information on the samples, provided by the original authors in their GEO/SRA submission and/or as Supplementary files in the pubblication.
Finally, the object contains a list of metadata that provide additional information on the experiment.
names(metadata(fluidigm))## [1] "sample_info" "clusters" "which_qc"metadata(fluidigm)$which_qc## [1] "NREADS" "NALIGNED"
## [3] "RALIGN" "TOTAL_DUP"
## [5] "PRIMER" "INSERT_SZ"
## [7] "INSERT_SZ_STD" "COMPLEXITY"
## [9] "NDUPR" "PCT_RIBOSOMAL_BASES"
## [11] "PCT_CODING_BASES" "PCT_UTR_BASES"
## [13] "PCT_INTRONIC_BASES" "PCT_INTERGENIC_BASES"
## [15] "PCT_MRNA_BASES" "MEDIAN_CV_COVERAGE"
## [17] "MEDIAN_5PRIME_BIAS" "MEDIAN_3PRIME_BIAS"
## [19] "MEDIAN_5PRIME_TO_3PRIME_BIAS"In particular, in all datasets, the metadata list includes an element called which_qc that contains the names of the colData columns that relate to sample QC.
The th2 and allen datasets contain the expression of the ERCC spike-ins. Note that these are included in the same matrices as the endogenous genes, hence users must use caution to avoid when using the data, to avoid mistreat external spike-ins as endogenous genes. One may wish to split the datasets in two, e.g.:
data(th2)
ercc_idx <- grep("^ERCC-", rownames(th2))
th2_endogenous <- th2[-ercc_idx,]
th2_ercc <- th2[ercc_idx,]
head(assay(th2_ercc)[,1:4])## ERR488983 ERR488967 ERR488989 ERR489021
## ERCC-00002 7775 14356 3868 15478
## ERCC-00003 1 75 1 2114
## ERCC-00004 1167 2468 1960 3914
## ERCC-00009 237 4 1167 1318
## ERCC-00012 0 0 0 0
## ERCC-00013 0 0 0 0Li, B, and CN Dewey. 2011. “RSEM: Accurate Transcript Quantification from RNA-Seq Data with or Without a Reference Genome.” BMC Bioinformatics 12 (1): 1.
Liao, Y, GK Smyth, and W Shi. 2013. “featureCounts: An Efficient General Purpose Program for Assigning Sequence Reads to Genomic Features.” Bioinformatics, btt656.
Mahata, B, X Zhang, AA Kolodziejczyk, V Proserpio, L Haim-Vilmovsky, AE Taylor, D Hebenstreit, et al. 2014. “Single-Cell RNA Sequencing Reveals T Helper Cells Synthesizing Steroids de Novo to Contribute to Immune Homeostasis.” Cell Reports 7 (4): 1130–42.
Pollen, AA, TJ Nowakowski, J Shuga, X Wang, AA Leyrat, JH Lui, N Li, et al. 2014. “Low-Coverage Single-Cell mRNA Sequencing Reveals Cellular Heterogeneity and Activated Signaling Pathways in Developing Cerebral Cortex.” Nature Biotechnology 32 (10): 1053–8.
Tasic, B, V Menon, TN Nguyen, TK Kim, T Jarsky, Z Yao, B Levi, et al. 2016. “Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics.” Nature Neuroscience 19: 335–46.
Trapnell, C, L Pachter, and SL Salzberg. 2009. “TopHat: Discovering Splice Junctions with RNA-Seq.” Bioinformatics 25 (9): 1105–11.
Trapnell, C, BA Williams, G Pertea, A Mortazavi, G Kwan, MJ Van Baren, SL Salzberg, BJ Wold, and L Pachter. 2010. “Transcript Assembly and Quantification by RNA-Seq Reveals Unannotated Transcripts and Isoform Switching During Cell Differentiation.” Nature Biotechnology 28 (5): 511–15.