conquer
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    About conquer

    The conquer (consistent quantification of external rna-seq data) repository is developed by Charlotte Soneson and Mark D Robinson at the University of Zurich, Switzerland. It is implemented in shiny and provides access to consistently processed public single-cell RNA-seq data sets. Below is a short description of the workflow used to process the raw reads in order to generate the data provided in the repository.

    If you use conquer for your work, please cite

    • C Soneson & MD Robinson: Bias, robustness and scalability in single-cell differential expression analysis. Nature Methods 15(4):255-261 (2018).

    The information provided in the columns Brief description, Protocol and Protocol type was inferred and summarized from the information provided by the data generators in the public repositories. We refer to the original descriptions for more detailed information.

    Index building

    In order to use Salmon to quantify the transcript abundances in a given sample, we first need to index the corresponding reference transcriptome. For a given organism, we download the fasta files containing cDNA and ncRNA sequences from Ensembl, complement these with ERCC spike-in sequences, and build a Salmon quasi-mapping index for the entire catalog. Note that the scater report for a given data set (available in the scater report column) details the precise version of the transcriptome that was used for the quantification. For data sets with “long” reads (longer than 50 bp) we use the default k=31, while for “short reads” (typically around 25 bp) we set k=15.

    We also create a lookup table relating transcript IDs to the corresponding gene IDs. This information is obtained by parsing the sequence names in the cDNA and ncRNA fasta files. From these names we also obtain the genomic coordinates for each feature.

    Sample list and run matching

    The first step is to determine the set of samples included in a given data set. We download a “RunInfo.csv” file for the data set from SRA and a Series Matrix file from GEO, in order to link samples both to individual runs and to phenotypic information. If the data set is not available from GEO, we construct a phenotype data file from the information provided by the corresponding repository.

    Quality control

    For each sample in the data set, we find all the corresponding runs, and download and concatenate the corresponding FastQ files from SRA. There is also an optional step to trim adapters from the reads using cutadapt. Next, we run FastQC to generate a quality control file for each concatenated read file (one or two files per sample depending on whether it was processed with a single-end or paired-end sequencing protocol).

    Abundance quantification

    After the QC, we run Salmon to estimate the abundance of each transcript from the catalog described above in each sample. The Salmon output files are then compressed in an archive and can be downloaded from conquer (see the salmon archive column).

    For data obtained with non-full-length library preparation protocols (e.g. targeting only the 3' or 5' end of transcripts), we quantify transcript and gene abundances using the umis pipeline developed by Valentine Svensson. Briefly, we quasimap the reads to the transcriptome using RapMap and use the counting capabilities of umis to obtain feature counts.

    Summary report - MultiQC

    Once FastQC and Salmon (or RapMap/umis) have been applied to all samples in the data set, we run MultiQC to summarise all the information into one report. This can also be downloaded from conquer (see the MultiQC report column). This report contains quality scores for all the samples and can be used to determine if there are problematic samples and whether the data set is good enough for the purposes of the user or needs to be subsetted.

    Data summarisation

    The abundances estimated by Salmon are summarised and provided to the user via conquer in the form of a MultiAssayExperiment object. This object can be downloaded via the buttons in the MultiAssayExperiment column. To generate this object, we first use the tximport package to read the Salmon output into R. This returns both count estimates and TPM estimates for each transcript. Next, we summarise the transcript-level information to the gene level. The gene-level TPM is defined as the sum of the TPMs of the corresponding transcripts, and similarly for the gene-level counts. We also provide “scaled TPMs” (see http://f1000research.com/articles/4-1521/ or the tximport vignette for a discussion), that is, summarised TPMs scaled to a “count scale”. In the summarisation step, we make use of the transcript-to-gene lookup table generated above.

    The provided MultiAssayExperiment object contains two “experiments”, corresponding to the gene-level and transcript-level values. The gene-level experiment contains four “assays”:

    • TPM
    • count
    • count_lstpm (count-scale length-scaled TPMs)
    • avetxlength (the average transcript length, which can be used as offsets in count models based on the count assay, see http://f1000research.com/articles/4-1521/).

    The transcript-level experiment contains three “assays”:

    • TPM
    • count
    • efflength (the effective length estimated by Salmon)

    The MultiAssayExperiment also contains the phenotypic data (in the colData slot), as well as some metadata for the data set (the genome, the organism, a summary of the Salmon parameters and the fraction of reads that were mapped, and the date when the object was generated). Please note that the format of MultiAssayExperiment objects changed with version 1.1.49 of the MultiAssayExperiment package, and in particular the pData slot is now deprecated in favor of colData. The objects provided in conquer follow the new format.

    Summary report - scater

    In order to give users another way of investigating whether a data set is useful for their purposes, we also provide an exploratory analysis report. This is largely based on functions from the scater Bioconductor package, applied to data extracted from the MultiAssayExperiment object. The report calculates and visualises various quality measures for the cells, and provides low-dimensional representations of the cells, colored by different phenotypic annotations.

    Acknowledgements

    We would like to thank Simon Andrews for help with FastQC, Mike Love and Valentine Svensson for providing instructions for how to retrieve the URL for the FastQ file(s) of a given SRA run (see here and here), Davis McCarthy for input regarding scater and Nicholas Hamilton for instructions on how to generate a standardized report based on a provided R object (see here)). Finally, we would like to acknowledge the developers of all the tools we use to prepare the data for conquer.

    Presentations/publications

    conquer was presented as a poster at the Single Cell Genomics conference in Hinxton, UK, in September 2016. A detailed description of the database and an example of its use in an evaluation of differential expression analysis methods for single-cell RNA-seq data can be found in:

    • C Soneson & MD Robinson: Bias, robustness and scalability in differential expression analysis of single-cell RNA-seq data. bioRxiv doi:10.1101/143289 (2017).

    Code

    The code used for conquer is available via GitHub.

    Changelog

    • 2017-07-22: Updated to Bioconductor 3.5 (affects MultiAssayExperiment objects and scater results). Updated MultiQC to version 1.1
    • 2016-10-27: Updated to Bioconductor 3.4 (affects MultiAssayExperiment objects and scater results)
    • 2016-10-25: Updated MultiQC to version 0.8

    The following list contains the samples that were excluded from each of the data sets. Most of these samples were excluded since they do not represent single cells. In rare cases (indicated in italics), the download or processing of the sample failed.

    GSE45719

    GSM1112582, GSM1112583, GSM1112584, GSM1112585, GSM1112586, GSM1112587, GSM1112588, GSM1112589, GSM1278009, GSM1278010, GSM1278011, GSM1278012, GSM1278013, GSM1278014, GSM1278015, GSM1278016, GSM1278026, GSM1278027, GSM1278028, GSM1278029, GSM1278030, GSM1278031, GSM1278032, GSM1278033, GSM1278034, GSM1278035

    GSE60749-GPL13112

    GSM1487049, GSM1487050, GSM1487051, GSM1487052, GSM1487053, GSM1487054, GSM1487055, GSM1487056, GSM1487057, GSM1487058, GSM1487059, GSM1487060, GSM1487061, GSM1487062, GSM1487063, GSM1487064, GSM1487065, GSM1487066, GSM1487067, GSM1487068, GSM1487069, GSM1487070, GSM1487071, GSM1487072, GSM1487073, GSM1487074

    GSE57872

    GSM1396263, GSM1396264, GSM1396265, GSM1396266, GSM1396267, GSM1396268, GSM1396269, GSM1396270, GSM1396271, GSM1396272, GSM1396273

    GSE48968

    GSM1190890, GSM1190891, GSM1190892, GSM1190893, GSM1190894, GSM1190895, GSM1190896, GSM1190897, GSM1190898, GSM1190899, GSM1190900, GSM1190901, GSM1190902

    GSE41265

    GSM1012795, GSM1012796, GSM1012797, GSM1110889, GSM1110890, GSM1110891

    GSE44183-GPL11154

    GSM1080212

    GSE52529-GPL16791

    GSM1269332, GSM1269333, GSM1269334, GSM1269335, GSM1269336, GSM1269337, GSM1269338, GSM1269339, GSM1269340, GSM1269341, GSM1269342, GSM1269343

    GSE63818

    GSM1677801, GSM1677802, GSM1677803, GSM1677804, GSM1677805, GSM1677806, GSM1677807, GSM1677808, GSM1677809, GSM1677810, GSM1677811, GSM1677812, GSM1677813, GSM1677814, GSM1677815, GSM1677816, GSM1677817, GSM1677818, GSM1677819, GSM1677820, GSM1677821, GSM1677822, GSM1677823, GSM1677824, GSM1677825, GSM1677826, GSM1677827, GSM1677828, GSM1677829, GSM1677830, GSM1677831, GSM1677832, GSM1677833, GSM1677834, GSM1677835, GSM1677836

    GSE71585-GPL13112

    GSM1840998, GSM1840999, GSM1841000, GSM1839229, GSM1839230

    GSE71585-GPL17021

    GSE1840992, GSM1840993, GSM1840994, GSM1840995, GSM1840996, GSM1840997, GSM1840931, GSM1840932, GSM1840933, GSM1840934, GSM1840935, GSM1840936, GSM1840937, GSM1840938, GSM1840939, GSM1840940, GSM1840941, GSM1840942, GSM1840943

    GSE100911

    GSM2696330

    GSE80232

    GSM2121581, GSM2121582, GSM2121583

    GSE84465

    GSM2244841, GSM2244965, GSM2245176, GSM2245437, GSM2246972

    Using the conquer database

    Note! Starting from version 1.1.49, the pData slot in a MultiAssayExperiment is deprecated in favor of colData. The objects included in conquer are now updated to the new version.

    To use a data set provided in the conquer database, download the corresponding R object from the MultiAssayExperiment column. As an illustration, we will assume that the file for the GSE41265 data set has been downloaded and is available in the current working directory. First, load the SummarizedExperiment and MultiAssayExperiment packages and read the file into R:

    suppressPackageStartupMessages(library(SummarizedExperiment))
    suppressPackageStartupMessages(library(MultiAssayExperiment))
    (gse41265 <- readRDS("GSE41265.rds"))
    
    ## A MultiAssayExperiment object of 2 listed
    ##  experiments with user-defined names and respective classes. 
    ##  Containing an ExperimentList class object of length 2: 
    ##  [1] gene: RangedSummarizedExperiment with 45686 rows and 18 columns 
    ##  [2] tx: RangedSummarizedExperiment with 113560 rows and 18 columns 
    ## Features: 
    ##  experiments() - obtain the ExperimentList instance 
    ##  colData() - the primary/phenotype DataFrame 
    ##  sampleMap() - the sample availability DataFrame 
    ##  `$`, `[`, `[[` - extract colData columns, subset, or experiment 
    ##  *Format() - convert into a long or wide DataFrame 
    ##  assays() - convert ExperimentList to a SimpleList of matrices
    

    The resulting object contains both gene and transcript abundances.

    experiments(gse41265)
    
    ## ExperimentList class object of length 2: 
    ##  [1] gene: RangedSummarizedExperiment with 45686 rows and 18 columns 
    ##  [2] tx: RangedSummarizedExperiment with 113560 rows and 18 columns
    

    Gene-level data

    To access the gene abundances, get the gene experiment:

    (gse41265_gene <- experiments(gse41265)[["gene"]])
    
    ## class: RangedSummarizedExperiment 
    ## dim: 45686 18 
    ## metadata(0):
    ## assays(4): TPM count count_lstpm avetxlength
    ## rownames(45686): ENSMUSG00000000001.4 ENSMUSG00000000003.15 ...
    ##   ERCC-00170 ERCC-00171
    ## rowData names(3): gene genome symbol
    ## colnames(18): GSM1012777 GSM1012778 ... GSM1012793 GSM1012794
    ## colData names(0):
    

    This object contains four slots, which can be accessed via the assays function:

    • TPM: transcripts per million abundance estimates for each gene, obtained by summing the transcript TPMs for the gene's isoforms.
    • count: gene read counts, obtained by summing the estimated read counts for the gene's isoforms.
    • count_lstpm: length-scaled TPMs, which provide and alternative abundance measure on the “count scale”, which is not correlated with the average transcript length in a given sample. See the tximport Bioconductor package for more information.
    • avetxlength: the average length of the transcripts expressed in each sample for each gene. See the tximport Bioconductor package for more information.

    Each of these slots is a matrix with the respective values for each gene and each sample.

    head(assays(gse41265_gene)[["TPM"]])
    
    ##                       GSM1012777 GSM1012778 GSM1012779 GSM1012780
    ## ENSMUSG00000000001.4     16.9260 68.9189000 27.2136000    25.6035
    ## ENSMUSG00000000003.15     0.0000  0.0000000  0.0000000     0.0000
    ## ENSMUSG00000000028.14    84.6329  0.0354825  0.0261671     0.0000
    ## ENSMUSG00000000031.15     0.0000  0.0000000  0.0000000     0.0000
    ## ENSMUSG00000000037.16    21.0225  0.0000000  0.0000000     0.0000
    ## ENSMUSG00000000049.11     0.0000  0.0000000  0.0000000     0.0000
    ##                       GSM1012781 GSM1012782 GSM1012783 GSM1012784
    ## ENSMUSG00000000001.4    1.003940 13.9319000    1.62186   27.26950
    ## ENSMUSG00000000003.15   0.000000  0.0000000    0.00000    0.00000
    ## ENSMUSG00000000028.14   0.028031  0.0816208    0.00000   18.28858
    ## ENSMUSG00000000031.15   0.000000  0.0000000    0.00000    0.00000
    ## ENSMUSG00000000037.16   0.000000  0.0000000    0.00000    0.00000
    ## ENSMUSG00000000049.11   0.000000  0.0000000    0.00000    0.00000
    ##                       GSM1012785 GSM1012786 GSM1012787 GSM1012788
    ## ENSMUSG00000000001.4    0.220791    0.14094    21.8415  42.991200
    ## ENSMUSG00000000003.15   0.000000    0.00000     0.0000   0.000000
    ## ENSMUSG00000000028.14  58.226400    0.00000     0.0000   0.118938
    ## ENSMUSG00000000031.15   0.000000    0.00000     0.0000   0.000000
    ## ENSMUSG00000000037.16   0.000000    0.00000     0.0000   0.000000
    ## ENSMUSG00000000049.11   0.000000    0.00000     0.0000   0.000000
    ##                       GSM1012789 GSM1012790 GSM1012791 GSM1012792
    ## ENSMUSG00000000001.4     156.447   0.109962  1.3144600 27.3401000
    ## ENSMUSG00000000003.15      0.000   0.000000  0.0000000  0.0000000
    ## ENSMUSG00000000028.14      0.000   3.537801  0.0382929  0.0340262
    ## ENSMUSG00000000031.15      0.000   0.000000  0.0000000  0.0000000
    ## ENSMUSG00000000037.16      0.000   0.000000  0.0000000  0.5705780
    ## ENSMUSG00000000049.11      0.000   0.000000  0.0000000  0.8658010
    ##                       GSM1012793 GSM1012794
    ## ENSMUSG00000000001.4     23.0052 32.8455000
    ## ENSMUSG00000000003.15     0.0000  0.0000000
    ## ENSMUSG00000000028.14     0.0000  0.0453766
    ## ENSMUSG00000000031.15     0.0000  0.0000000
    ## ENSMUSG00000000037.16     0.0000  0.0000000
    ## ENSMUSG00000000049.11     0.0000  0.0000000
    
    head(assays(gse41265_gene)[["count"]])
    
    ##                       GSM1012777 GSM1012778 GSM1012779 GSM1012780
    ## ENSMUSG00000000001.4     777.612    3903.94    2088.36    1493.55
    ## ENSMUSG00000000003.15      0.000       0.00       0.00       0.00
    ## ENSMUSG00000000028.14   2352.123       1.00       1.00       0.00
    ## ENSMUSG00000000031.15      0.000       0.00       0.00       0.00
    ## ENSMUSG00000000037.16     90.000       0.00       0.00       0.00
    ## ENSMUSG00000000049.11      0.000       0.00       0.00       0.00
    ##                       GSM1012781 GSM1012782 GSM1012783 GSM1012784
    ## ENSMUSG00000000001.4     71.8391 875.125000    93.6992   1645.560
    ## ENSMUSG00000000003.15     0.0000   0.000000     0.0000      0.000
    ## ENSMUSG00000000028.14     1.0000   2.000001     0.0000    672.918
    ## ENSMUSG00000000031.15     0.0000   0.000000     0.0000      0.000
    ## ENSMUSG00000000037.16     0.0000   0.000000     0.0000      0.000
    ## ENSMUSG00000000049.11     0.0000   0.000000     0.0000      0.000
    ##                       GSM1012785 GSM1012786 GSM1012787 GSM1012788
    ## ENSMUSG00000000001.4       11.00          9    1515.99    1843.77
    ## ENSMUSG00000000003.15       0.00          0       0.00       0.00
    ## ENSMUSG00000000028.14    1823.75          0       0.00       1.00
    ## ENSMUSG00000000031.15       0.00          0       0.00       0.00
    ## ENSMUSG00000000037.16       0.00          0       0.00       0.00
    ## ENSMUSG00000000049.11       0.00          0       0.00       0.00
    ##                       GSM1012789 GSM1012790 GSM1012791 GSM1012792
    ## ENSMUSG00000000001.4     9756.93     6.0000    69.0064    1614.74
    ## ENSMUSG00000000003.15       0.00     0.0000     0.0000       0.00
    ## ENSMUSG00000000028.14       0.00   121.1216     1.0000       1.00
    ## ENSMUSG00000000031.15       0.00     0.0000     0.0000       0.00
    ## ENSMUSG00000000037.16       0.00     0.0000     0.0000      30.00
    ## ENSMUSG00000000049.11       0.00     0.0000     0.0000      16.00
    ##                       GSM1012793 GSM1012794
    ## ENSMUSG00000000001.4     1277.01     1459.1
    ## ENSMUSG00000000003.15       0.00        0.0
    ## ENSMUSG00000000028.14       0.00        1.0
    ## ENSMUSG00000000031.15       0.00        0.0
    ## ENSMUSG00000000037.16       0.00        0.0
    ## ENSMUSG00000000049.11       0.00        0.0
    
    head(assays(gse41265_gene)[["count_lstpm"]])
    
    ##                       GSM1012777   GSM1012778   GSM1012779 GSM1012780
    ## ENSMUSG00000000001.4    752.8798 3720.7822146 2032.4459094   1452.948
    ## ENSMUSG00000000003.15     0.0000    0.0000000    0.0000000      0.000
    ## ENSMUSG00000000028.14  1872.5288    0.9528578    0.9720902      0.000
    ## ENSMUSG00000000031.15     0.0000    0.0000000    0.0000000      0.000
    ## ENSMUSG00000000037.16   290.8873    0.0000000    0.0000000      0.000
    ## ENSMUSG00000000049.11     0.0000    0.0000000    0.0000000      0.000
    ##                       GSM1012781 GSM1012782 GSM1012783 GSM1012784
    ## ENSMUSG00000000001.4  69.9132476 847.647213   91.01965  1582.7900
    ## ENSMUSG00000000003.15  0.0000000   0.000000    0.00000     0.0000
    ## ENSMUSG00000000028.14  0.9709756   2.470152    0.00000   528.0124
    ## ENSMUSG00000000031.15  0.0000000   0.000000    0.00000     0.0000
    ## ENSMUSG00000000037.16  0.0000000   0.000000    0.00000     0.0000
    ## ENSMUSG00000000049.11  0.0000000   0.000000    0.00000     0.0000
    ##                       GSM1012785 GSM1012786 GSM1012787  GSM1012788
    ## ENSMUSG00000000001.4    10.61746   8.385097   1487.373 1497.153135
    ## ENSMUSG00000000003.15    0.00000   0.000000      0.000    0.000000
    ## ENSMUSG00000000028.14 1392.76246   0.000000      0.000    2.060275
    ## ENSMUSG00000000031.15    0.00000   0.000000      0.000    0.000000
    ## ENSMUSG00000000037.16    0.00000   0.000000      0.000    0.000000
    ## ENSMUSG00000000049.11    0.00000   0.000000      0.000    0.000000
    ##                       GSM1012789 GSM1012790 GSM1012791   GSM1012792
    ## ENSMUSG00000000001.4    9318.475   5.840962 66.3240941 1559.5448194
    ## ENSMUSG00000000003.15      0.000   0.000000  0.0000000    0.0000000
    ## ENSMUSG00000000028.14      0.000  93.474485  0.9610815    0.9654487
    ## ENSMUSG00000000031.15      0.000   0.000000  0.0000000    0.0000000
    ## ENSMUSG00000000037.16      0.000   0.000000  0.0000000   10.1246935
    ## ENSMUSG00000000049.11      0.000   0.000000  0.0000000   15.4503225
    ##                       GSM1012793   GSM1012794
    ## ENSMUSG00000000001.4    1201.013 1407.2935134
    ## ENSMUSG00000000003.15      0.000    0.0000000
    ## ENSMUSG00000000028.14      0.000    0.9670719
    ## ENSMUSG00000000031.15      0.000    0.0000000
    ## ENSMUSG00000000037.16      0.000    0.0000000
    ## ENSMUSG00000000049.11      0.000    0.0000000
    
    head(assays(gse41265_gene)[["avetxlength"]])
    
    ##                       GSM1012777 GSM1012778 GSM1012779 GSM1012780
    ## ENSMUSG00000000001.4   3026.4100  3015.1200  3017.9000  3018.6900
    ## ENSMUSG00000000003.15   553.6516   553.6516   553.6516   553.6516
    ## ENSMUSG00000000028.14  1830.7909  1500.1200  1502.9000  1462.8034
    ## ENSMUSG00000000031.15  1022.8460  1022.8460  1022.8460  1022.8460
    ## ENSMUSG00000000037.16   282.0180   870.1116   870.1116   870.1116
    ## ENSMUSG00000000049.11   943.5560   943.5560   943.5560   943.5560
    ##                       GSM1012781 GSM1012782 GSM1012783 GSM1012784
    ## ENSMUSG00000000001.4   3021.2400  3018.8100  3015.9000  3014.2200
    ## ENSMUSG00000000003.15   553.6516   553.6516   553.6516   553.6516
    ## ENSMUSG00000000028.14  1506.2400  1177.6154  1462.8034  1837.8923
    ## ENSMUSG00000000031.15  1022.8460  1022.8460  1022.8460  1022.8460
    ## ENSMUSG00000000037.16   870.1116   870.1116   870.1116   870.1116
    ## ENSMUSG00000000049.11   943.5560   943.5560   943.5560   943.5560
    ##                       GSM1012785 GSM1012786 GSM1012787 GSM1012788
    ## ENSMUSG00000000001.4   3013.6100  3005.7400  3017.4300  3022.5500
    ## ENSMUSG00000000003.15   553.6516   553.6516   553.6516   553.6516
    ## ENSMUSG00000000028.14  1894.6100  1462.8034  1462.8034   592.5530
    ## ENSMUSG00000000031.15  1022.8460  1022.8460  1022.8460  1022.8460
    ## ENSMUSG00000000037.16   870.1116   870.1116   870.1116   870.1116
    ## ENSMUSG00000000049.11   943.5560   943.5560   943.5560   943.5560
    ##                       GSM1012789 GSM1012790 GSM1012791 GSM1012792
    ## ENSMUSG00000000001.4   3015.2300  3017.5800  3014.5600  3015.5600
    ## ENSMUSG00000000003.15   553.6516   553.6516   553.6516   553.6516
    ## ENSMUSG00000000028.14  1462.8034  1893.3784  1499.5600  1500.5600
    ## ENSMUSG00000000031.15  1022.8460  1022.8460  1022.8460  1022.8460
    ## ENSMUSG00000000037.16   870.1116   870.1116   870.1116  2684.5600
    ## ENSMUSG00000000049.11   943.5560   943.5560   943.5560   943.5560
    ##                       GSM1012793 GSM1012794
    ## ENSMUSG00000000001.4   3012.8000  3006.4800
    ## ENSMUSG00000000003.15   553.6516   553.6516
    ## ENSMUSG00000000028.14  1462.8034  1491.4800
    ## ENSMUSG00000000031.15  1022.8460  1022.8460
    ## ENSMUSG00000000037.16   870.1116   870.1116
    ## ENSMUSG00000000049.11   943.5560   943.5560
    

    Transcript-level data

    To access the transcript abundances, get instead the transcript experiment:

    (gse41265_tx <- experiments(gse41265)[["tx"]])
    
    ## class: RangedSummarizedExperiment 
    ## dim: 113560 18 
    ## metadata(0):
    ## assays(3): TPM count efflength
    ## rownames(113560): ENSMUST00000178537.1 ENSMUST00000178862.1 ...
    ##   ERCC-00170 ERCC-00171
    ## rowData names(4): transcript gene genome symbol
    ## colnames(18): GSM1012777 GSM1012778 ... GSM1012793 GSM1012794
    ## colData names(0):
    

    This object contains three slots, which can be accessed via the assays function:

    • TPM: transcripts per million abundance estimates for each transcript.
    • count: transcript read counts.
    • efflength: effective transcript lengths.

    Each of these slots is a matrix with the respective values for each transcript and each sample.

    head(assays(gse41265_tx)[["TPM"]])
    
    ##                      GSM1012777 GSM1012778 GSM1012779 GSM1012780
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012781 GSM1012782 GSM1012783 GSM1012784
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012785 GSM1012786 GSM1012787 GSM1012788
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012789 GSM1012790 GSM1012791 GSM1012792
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012793 GSM1012794
    ## ENSMUST00000178537.1          0          0
    ## ENSMUST00000178862.1          0          0
    ## ENSMUST00000177564.1          0          0
    ## ENSMUST00000196221.1          0          0
    ## ENSMUST00000179664.1          0          0
    ## ENSMUST00000179520.1          0          0
    
    head(assays(gse41265_tx)[["count"]])
    
    ##                      GSM1012777 GSM1012778 GSM1012779 GSM1012780
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012781 GSM1012782 GSM1012783 GSM1012784
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012785 GSM1012786 GSM1012787 GSM1012788
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012789 GSM1012790 GSM1012791 GSM1012792
    ## ENSMUST00000178537.1          0          0          0          0
    ## ENSMUST00000178862.1          0          0          0          0
    ## ENSMUST00000177564.1          0          0          0          0
    ## ENSMUST00000196221.1          0          0          0          0
    ## ENSMUST00000179664.1          0          0          0          0
    ## ENSMUST00000179520.1          0          0          0          0
    ##                      GSM1012793 GSM1012794
    ## ENSMUST00000178537.1          0          0
    ## ENSMUST00000178862.1          0          0
    ## ENSMUST00000177564.1          0          0
    ## ENSMUST00000196221.1          0          0
    ## ENSMUST00000179664.1          0          0
    ## ENSMUST00000179520.1          0          0
    
    head(assays(gse41265_tx)[["efflength"]])
    
    ##                      GSM1012777 GSM1012778 GSM1012779 GSM1012780
    ## ENSMUST00000178537.1         12         12         12         12
    ## ENSMUST00000178862.1         14         14         14         14
    ## ENSMUST00000177564.1         16         16         16         16
    ## ENSMUST00000196221.1          9          9          9          9
    ## ENSMUST00000179664.1         11         11         11         11
    ## ENSMUST00000179520.1         11         11         11         11
    ##                      GSM1012781 GSM1012782 GSM1012783 GSM1012784
    ## ENSMUST00000178537.1         12         12         12         12
    ## ENSMUST00000178862.1         14         14         14         14
    ## ENSMUST00000177564.1         16         16         16         16
    ## ENSMUST00000196221.1          9          9          9          9
    ## ENSMUST00000179664.1         11         11         11         11
    ## ENSMUST00000179520.1         11         11         11         11
    ##                      GSM1012785 GSM1012786 GSM1012787 GSM1012788
    ## ENSMUST00000178537.1         12         12         12         12
    ## ENSMUST00000178862.1         14         14         14         14
    ## ENSMUST00000177564.1         16         16         16         16
    ## ENSMUST00000196221.1          9          9          9          9
    ## ENSMUST00000179664.1         11         11         11         11
    ## ENSMUST00000179520.1         11         11         11         11
    ##                      GSM1012789 GSM1012790 GSM1012791 GSM1012792
    ## ENSMUST00000178537.1         12         12         12         12
    ## ENSMUST00000178862.1         14         14         14         14
    ## ENSMUST00000177564.1         16         16         16         16
    ## ENSMUST00000196221.1          9          9          9          9
    ## ENSMUST00000179664.1         11         11         11         11
    ## ENSMUST00000179520.1         11         11         11         11
    ##                      GSM1012793 GSM1012794
    ## ENSMUST00000178537.1         12         12
    ## ENSMUST00000178862.1         14         14
    ## ENSMUST00000177564.1         16         16
    ## ENSMUST00000196221.1          9          9
    ## ENSMUST00000179664.1         11         11
    ## ENSMUST00000179520.1         11         11
    

    Sample annotations

    The sample annotations, downloaded from GEO, are also available in the object:

    pdata <- colData(gse41265)
    head(pdata, 2)
    
    ## DataFrame with 2 rows and 47 columns
    ##                     title geo_accession                status
    ##                  <factor>      <factor>              <factor>
    ## GSM1012777 Single cell S1    GSM1012777 Public on May 19 2013
    ## GSM1012778 Single cell S2    GSM1012778 Public on May 19 2013
    ##            submission_date last_update_date     type channel_count
    ##                   <factor>         <factor> <factor>      <factor>
    ## GSM1012777     Oct 01 2012      May 19 2013      SRA             1
    ## GSM1012778     Oct 01 2012      May 19 2013      SRA             1
    ##               source_name_ch1 organism_ch1 characteristics_ch1
    ##                      <factor>     <factor>            <factor>
    ## GSM1012777 BMDC (4h LPS stim) Mus musculus     strain: C57BL/6
    ## GSM1012778 BMDC (4h LPS stim) Mus musculus     strain: C57BL/6
    ##                                           characteristics_ch1.1
    ##                                                        <factor>
    ## GSM1012777 cell type: Bone Marrow-derived Dendritic Cell (BMDC)
    ## GSM1012778 cell type: Bone Marrow-derived Dendritic Cell (BMDC)
    ##                 characteristics_ch1.2 characteristics_ch1.3
    ##                              <factor>              <factor>
    ## GSM1012777 treatment: LPS-stimulation    cell count: 1 cell
    ## GSM1012778 treatment: LPS-stimulation    cell count: 1 cell
    ##            characteristics_ch1.4
    ##                         <factor>
    ## GSM1012777                      
    ## GSM1012778                      
    ##                                                                               growth_protocol_ch1
    ##                                                                                          <factor>
    ## GSM1012777 Cells were cultured and stimulated with LPS as previously described (Amit et. al 2009)
    ## GSM1012778 Cells were cultured and stimulated with LPS as previously described (Amit et. al 2009)
    ##            molecule_ch1              extract_protocol_ch1
    ##                <factor>                          <factor>
    ## GSM1012777    polyA RNA cDNA synthesis and amplification:
    ## GSM1012778    polyA RNA cDNA synthesis and amplification:
    ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           extract_protocol_ch1.1
    ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         <factor>
    ## GSM1012777 We used the SMARTer Ultra Low RNA Kit (Clontech, Mountain View, CA) to prepare amplified cDNA. We added 1   l of 12   M 3' SMART primer (5'   AAGCAGTGGTATCAACGCAGAGTACT(30)N-1N (N = A, C, G, or T; N-1 = A, G, or C)), 1   l of H2O, and 2.5   l of Reaction Buffer onto the RNA capture beads. We mixed them well by pipetting, heated the mixture at 72  C for 3 minutes and placed it on ice. First-strand cDNA was synthesized with this RNA primer mix by adding 2   l of 5x first-strand buffer, 0.25   l of 100mM DTT, 1   l of 10 mM dNTPs, 1   l of 12   M SMARTer II A Oligo (5'   AAGCAGTGGTATCAACGCAGAGTACXXXXX (X = undisclosed base in the proprietary SMARTer oligo sequence)), 100 U SMARTScribe RT, and 10 U RNase Inhibitor in a total volume of 10   l and incubating at 42  C for 90 minutes followed by 10 minutes at 70  C. We purified the first strand cDNA by adding 25   l of room temperature AMPure XP SPRI beads (Beckman Coulter Genomics, Danvers, MA), mixing well by pipetting, incubating at room temperature for 8 minutes. We removed the supernatant from the beads after a good separation was established. We carried out all of the above steps in a PCR product   free clean room. We amplified the cDNA by adding 5   l of 10x Advantage 2 PCR Buffer, 2   l of 10 mM dNTPs, 2   l of 12   M IS PCR primer (5'    AAGCAGTGGTATCAACGCAGAGT), 2   l of 50x Advantage 2 Polymerase Mix, and 39   l H2O in a total volume of 50   l. We performed the PCR at 95  C for 1 minute, followed by 21 cycles of 15 seconds at 95  C, 30 seconds at 65  C and 6 minutes at 68  C, followed by another 10 minutes at 72  C for final extension. We purified the amplified cDNA by adding 90   l of AMPure XP SPRI beads and washing with 80% ethanol.
    ## GSM1012778 We used the SMARTer Ultra Low RNA Kit (Clontech, Mountain View, CA) to prepare amplified cDNA. We added 1   l of 12   M 3' SMART primer (5'   AAGCAGTGGTATCAACGCAGAGTACT(30)N-1N (N = A, C, G, or T; N-1 = A, G, or C)), 1   l of H2O, and 2.5   l of Reaction Buffer onto the RNA capture beads. We mixed them well by pipetting, heated the mixture at 72  C for 3 minutes and placed it on ice. First-strand cDNA was synthesized with this RNA primer mix by adding 2   l of 5x first-strand buffer, 0.25   l of 100mM DTT, 1   l of 10 mM dNTPs, 1   l of 12   M SMARTer II A Oligo (5'   AAGCAGTGGTATCAACGCAGAGTACXXXXX (X = undisclosed base in the proprietary SMARTer oligo sequence)), 100 U SMARTScribe RT, and 10 U RNase Inhibitor in a total volume of 10   l and incubating at 42  C for 90 minutes followed by 10 minutes at 70  C. We purified the first strand cDNA by adding 25   l of room temperature AMPure XP SPRI beads (Beckman Coulter Genomics, Danvers, MA), mixing well by pipetting, incubating at room temperature for 8 minutes. We removed the supernatant from the beads after a good separation was established. We carried out all of the above steps in a PCR product   free clean room. We amplified the cDNA by adding 5   l of 10x Advantage 2 PCR Buffer, 2   l of 10 mM dNTPs, 2   l of 12   M IS PCR primer (5'    AAGCAGTGGTATCAACGCAGAGT), 2   l of 50x Advantage 2 Polymerase Mix, and 39   l H2O in a total volume of 50   l. We performed the PCR at 95  C for 1 minute, followed by 21 cycles of 15 seconds at 95  C, 30 seconds at 65  C and 6 minutes at 68  C, followed by another 10 minutes at 72  C for final extension. We purified the amplified cDNA by adding 90   l of AMPure XP SPRI beads and washing with 80% ethanol.
    ##                                                                                 extract_protocol_ch1.2
    ##                                                                                               <factor>
    ## GSM1012777 We created Illumina sequencing libraries from this amplified cDNA using standard protocols.
    ## GSM1012778 We created Illumina sequencing libraries from this amplified cDNA using standard protocols.
    ##                             extract_protocol_ch1.3
    ##                                           <factor>
    ## GSM1012777 cDNA shearing and library construction:
    ## GSM1012778 cDNA shearing and library construction:
    ##                                                                                                                                                                                                                                                                                                                                         extract_protocol_ch1.4
    ##                                                                                                                                                                                                                                                                                                                                                       <factor>
    ## GSM1012777 We added the purification buffer (Clontech) to the amplified cDNA to make a total volume of 76   l. We sheared the cDNA in a 100   l tube with 10% Duty Cycle, 5% Intensity and 200 Cycles/Burst for 5 minutes in the frequency sweeping mode (Covaris S2 machine, Woburn, MA). We purified the sheared cDNA with 2.2 volumes AMPure XP SPRI beads.
    ## GSM1012778 We added the purification buffer (Clontech) to the amplified cDNA to make a total volume of 76   l. We sheared the cDNA in a 100   l tube with 10% Duty Cycle, 5% Intensity and 200 Cycles/Burst for 5 minutes in the frequency sweeping mode (Covaris S2 machine, Woburn, MA). We purified the sheared cDNA with 2.2 volumes AMPure XP SPRI beads.
    ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         extract_protocol_ch1.5
    ##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       <factor>
    ## GSM1012777 We prepared indexed paired-end libraries for Illumina sequencing as described (J. Z. Levin et al., Nature Methods 7, 709 (2010)., with the following modifications. First, we used a different indexing adaptor (containing an 8-base barcode) for each library. Second, we size-selected the ligation product by using two rounds of 0.7 volume of AMPure XP SPRI bead cleanup with the first round starting volume at 100   l. Third, we performed PCR with Phusion High-Fidelity DNA polymerase with GC buffer and 2 M betaine. Fourth, we used 55  C as the annealing temperature in PCR with the universal indexing primers (forward primer 5'-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC, reverse primer 5'-CAAGCAGAAGACGGCATACGAGAT). Fifth, we performed 12 cycles of PCR. Sixth, we removed PCR primers using two rounds of 1.0 volume of AMPure beads.
    ## GSM1012778 We prepared indexed paired-end libraries for Illumina sequencing as described (J. Z. Levin et al., Nature Methods 7, 709 (2010)., with the following modifications. First, we used a different indexing adaptor (containing an 8-base barcode) for each library. Second, we size-selected the ligation product by using two rounds of 0.7 volume of AMPure XP SPRI bead cleanup with the first round starting volume at 100   l. Third, we performed PCR with Phusion High-Fidelity DNA polymerase with GC buffer and 2 M betaine. Fourth, we used 55  C as the annealing temperature in PCR with the universal indexing primers (forward primer 5'-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGAC, reverse primer 5'-CAAGCAGAAGACGGCATACGAGAT). Fifth, we performed 12 cycles of PCR. Sixth, we removed PCR primers using two rounds of 1.0 volume of AMPure beads.
    ##            taxid_ch1 description
    ##             <factor>    <factor>
    ## GSM1012777     10090          S1
    ## GSM1012778     10090          S2
    ##                                                                                                                                                                                                                                           data_processing
    ##                                                                                                                                                                                                                                                  <factor>
    ## GSM1012777 We created a Bowtie index based on the UCSC knownGene (8) transcriptome, and aligned paired-end reads directly to this index using Bowtie v 0.12.7 with command line options -q --phred33-quals -n 2 -e 99999999 -l 25 -I 1 -X 1000 -a -m 200.
    ## GSM1012778 We created a Bowtie index based on the UCSC knownGene (8) transcriptome, and aligned paired-end reads directly to this index using Bowtie v 0.12.7 with command line options -q --phred33-quals -n 2 -e 99999999 -l 25 -I 1 -X 1000 -a -m 200.
    ##                                                                                                                                                                                                                                       data_processing.1
    ##                                                                                                                                                                                                                                                <factor>
    ## GSM1012777 Next, we ran RSEM v1.11 with default parameters on these alignments to estimate expression levels. RSEM’s gene level expression estimates (tau) were multiplied by 1,000,000 to obtain transcript per million (TPM) estimates for each gene.
    ## GSM1012778 Next, we ran RSEM v1.11 with default parameters on these alignments to estimate expression levels. RSEM’s gene level expression estimates (tau) were multiplied by 1,000,000 to obtain transcript per million (TPM) estimates for each gene.
    ##            data_processing.2
    ##                     <factor>
    ## GSM1012777 Genome_build: mm9
    ## GSM1012778 Genome_build: mm9
    ##                                                                                                                                                                                                                                                                                                        data_processing.3
    ##                                                                                                                                                                                                                                                                                                                 <factor>
    ## GSM1012777 Supplementary_files_format_and_content: File allGenesTPM.txt represents a matrix of gene expression estimates across all non-MolecularBarcode samples. File umbExp.txt represents a matrix of gene expression estimates across all MolecularBarcode samples.  Linked as supplementary files on Series record.
    ## GSM1012778 Supplementary_files_format_and_content: File allGenesTPM.txt represents a matrix of gene expression estimates across all non-MolecularBarcode samples. File umbExp.txt represents a matrix of gene expression estimates across all MolecularBarcode samples.  Linked as supplementary files on Series record.
    ##            platform_id  contact_name        contact_email contact_phone
    ##               <factor>      <factor>             <factor>      <factor>
    ## GSM1012777    GPL13112 Rahul,,Satija rsatija@nygenome.org    6177022468
    ## GSM1012778    GPL13112 Rahul,,Satija rsatija@nygenome.org    6177022468
    ##            contact_laboratory      contact_institute
    ##                      <factor>               <factor>
    ## GSM1012777         Satija Lab New York Genome Center
    ## GSM1012778         Satija Lab New York Genome Center
    ##                       contact_address  contact_city contact_state
    ##                              <factor>      <factor>      <factor>
    ## GSM1012777 101 Avenue of the Americas New York City            NY
    ## GSM1012778 101 Avenue of the Americas New York City            NY
    ##            contact_zip.postal_code contact_country data_row_count
    ##                           <factor>        <factor>       <factor>
    ## GSM1012777                   10013             USA              0
    ## GSM1012778                   10013             USA              0
    ##               instrument_model library_selection library_source
    ##                       <factor>          <factor>       <factor>
    ## GSM1012777 Illumina HiSeq 2000              cDNA transcriptomic
    ## GSM1012778 Illumina HiSeq 2000              cDNA transcriptomic
    ##            library_strategy
    ##                    <factor>
    ## GSM1012777          RNA-Seq
    ## GSM1012778          RNA-Seq
    ##                                                       relation
    ##                                                       <factor>
    ## GSM1012777 SRA: http://www.ncbi.nlm.nih.gov/sra?term=SRX190719
    ## GSM1012778 SRA: http://www.ncbi.nlm.nih.gov/sra?term=SRX190720
    ##                                                               relation.1
    ##                                                                 <factor>
    ## GSM1012777 BioSample: http://www.ncbi.nlm.nih.gov/biosample/SAMN01737621
    ## GSM1012778 BioSample: http://www.ncbi.nlm.nih.gov/biosample/SAMN01737622
    ##                                                                             supplementary_file_1
    ##                                                                                         <factor>
    ## GSM1012777 ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByExp/sra/SRX/SRX190/SRX190719
    ## GSM1012778 ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByExp/sra/SRX/SRX190/SRX190720
    

    Metadata

    Finally, the MultiAssayExperiment object contains information regarding the mapping and abundance estimation, as well as the date when it was generated.

    names(metadata(gse41265))
    
    ## [1] "genome"         "organism"       "salmon_summary" "creation_date"
    
    metadata(gse41265)$genome
    
    ## [1] "GRCm38.84"
    
    metadata(gse41265)$organism
    
    ## [1] "Mus musculus"
    
    head(metadata(gse41265)$salmon_summary)
    
    ##       sample salmon_version libtype
    ## 1 GSM1012777          0.6.0      IU
    ## 2 GSM1012778          0.6.0      IU
    ## 3 GSM1012779          0.6.0      IU
    ## 4 GSM1012780          0.6.0      IU
    ## 5 GSM1012781          0.6.0      IU
    ## 6 GSM1012782          0.6.0      IU
    ##                                           index seqBias num_processed
    ## 1 Mus_musculus.GRCm38.84.cdna.ncrna.ercc92.sidx   FALSE      21326048
    ## 2 Mus_musculus.GRCm38.84.cdna.ncrna.ercc92.sidx   FALSE      27434011
    ## 3 Mus_musculus.GRCm38.84.cdna.ncrna.ercc92.sidx   FALSE      31142391
    ## 4 Mus_musculus.GRCm38.84.cdna.ncrna.ercc92.sidx   FALSE      26231852
    ## 5 Mus_musculus.GRCm38.84.cdna.ncrna.ercc92.sidx   FALSE      29977214
    ## 6 Mus_musculus.GRCm38.84.cdna.ncrna.ercc92.sidx   FALSE      24148387
    ##   num_mapped percent_mapped
    ## 1   15794660         74.063
    ## 2   18787649         68.483
    ## 3   24373666         78.265
    ## 4   20122093         76.709
    ## 5   23706687         79.082
    ## 6   19342343         80.098
    
    metadata(gse41265)$creation_date
    
    ## [1] "Sun Jul 23 20:00:43 2017"