UMAP

Five nearest cells (click image):

Download
Scatter Plot
Download
Scatter Data
Five nearest cells (click image):

Download
Meta Plot
Download
Meta Data

Tables













Expression Plot

Download Scatter Plot
Download Scatter Data


































































































































































In Situ Projection


























































































































Facet UMAP

Download Scatter Plot










































































Dotplot

Download Dot Plot Download Dot Data

























































































































Heatmap

Download Heatmap Download Heatmap Data

























































































































Gene Differential Expression Tests

Tests run with across CellType, CellType (Predict), and Cluster and split by organism. The differential testing has been updated (yet again). This time to a "pseudoBulk" approach where gene counts are summed by study and the test of interest (e.g. Cluster). The summed counts are "bulk-like" in their statistical properties and we simply apply them to a DESeq2 based differential test where we use the study as a covariate.

Two types of contrasts are extracted: in Table 1 we show a comparison (e.g. Rod) against all other remaining (everything not rod) and in Table 2 we use a contrast run in a pair-wise manner where we test, for example, cluster 2 directly against against cluster 10.


Download all results as csv











Haystack

singleCellHaystack is a cluster or cell type independent method for identifying differentially expressed or "interesting" genes

Very briefly, it uses the DKL divergence across the scVI multidimensional space to find non-randomly expressed genes. The table is ordered by the log10(p value) calculated (lower is a lower p value). A higher D_KL score means that the genes is less randomly expressed. T counts sums the number of counts (higher is expressed in more cells).

The CellType(s) and Cluster columns are the "top" genes which are most differentially expressed in the comparison. The idea is to provide a quick way to see what CellType(s) or Cluster are driving the singleCellHaystack identified gene.











Data


The codebase for the creation of the scEiaD dataset is on github

If size not given, it is less than 1 GB

Run plae Locally

If you have 500GB (!) of free hard drive space, you can run plae on your own computer. Installation instructions are available in our Github repository (this is the codebase for the app you are using now).

Seurat Objects

  • All (~29 GB)
  • All (h5Seurat, ~32 GB)

    • AnnData Objects

  • All (~32 GB)

    • PseudoBulk Diff Testing Results

  • CellType testing (3.2 GB)
  • CellType (Predict) testing (3.5 GB)
  • Cluster testing (3.9 GB)

    • PseudoBulk Data Matrices (and Metadata)

  • Homo sapiens CellType Metadata
  • Homo sapiens CellType PseudoBulk Counts
  • Macaca fascicularis CellType Metadata
  • Macaca fascicularis CellType PseudoBulk Counts
  • Mus musculus CellType Metadata
  • Mus musculus CellType PseudoBulk Counts
  • Homo sapiens CellType_predict Metadata
  • Homo sapiens CellType_predict PseudoBulk Counts
  • Macaca fascicularis CellType_predict Metadata
  • Macaca fascicularis CellType_predict PseudoBulk Counts
  • Mus musculus CellType_predict Metadata
  • Mus musculus CellType_predict PseudoBulk Counts
  • Homo sapiens cluster Metadata
  • Homo sapiens cluster PseudoBulk Counts
  • Macaca fascicularis cluster Metadata
  • Macaca fascicularis cluster PseudoBulk Counts
  • Mus musculus cluster Metadata
  • Mus musculus cluster PseudoBulk Counts

    • Metadata

  • Cell Metadata

    • Counts

  • Kallisto counts, R sparse matrix (12 GB)
  • Kallisto counts, no filtering, R sparse matrix (15 GB)


















    • plae v0.95


    PLatform for Analysis of scEiad

    plae pronounced play logo. eye ball with arms running to slide made of retina cells

    What is scEiaD?

    single cell Eye in a Disk

    The light-sensitive portion of the eye is the retina. The retina itself is not a monolithic tissue - there are over 10 major cell types. The cones and rods which convert light into signal are supported by a wide variety of neural cell types with distinct roles in interpretting and transmitting the visual signal to the brain. Behind the retina is the RPE and vasculature, which supports the high energetic needs of the rods and cones. In front of the retina is the clear lens and cornea, which shape the light onto the retina. scEiaD is a meta-atlas that compiles 1.1 million single-cell eye and body tissue transcriptomes across 44 datasets, 35 publications, and 4 species. Deep metadata mining, rigorous quality control analysis, differential gene expression testing, and deep learning based batch effect correction in a unified bioinformatic framework allow the universe of ocular single cell expression information to be analyzed in one location.

    tldr

    You can look up gene expression by retina cell type across loads of different studies, four organisms, and multiple developmental stages.

    How to cite?

    The article covering of the data creation and benchmarking of version 0.74 data is now publised at (now **not** on plae, but the codebase and principles are the same) GigaScience!

    Licensing

    This work is released under the CC0 license

    Data Sources

    Citation PMID SRA Accession organism Platform Count Labels
    Yamagata M, Yan W, Sanes JR. A ... 33393903 SRP286543 Gallus gallus 10xv2 37498 Yes
    Collin J, Queen R, Zerti D, Bo ... 33865984 SRP275814 Homo sapiens 10xv2 100472 Yes
    He S, Wang LH, Liu Y, Li YQ et ... 33287869 SRP292721 Homo sapiens 10xv2 79245 Yes
    Lu Y, Shiau F, Yi W, Lu S et a ... 32386599 SRP223254 Homo sapiens 10xv2 64425 Yes
    Cowan CS, Renner M, De Gennaro ... 32946783 EGAD00001006350 Homo sapiens 10xv2 55018 Yes
    Lu Y, Shiau F, Yi W, Lu S et a ... 32386599 SRP151023 Homo sapiens 10xv2 42827 Yes
    Yan W, Peng YR, van Zyl T, Reg ... 32555229 SRP255195 Homo sapiens 10xv2 25737 No
    Voigt AP, Whitmore SS, Mulfaul ... 32531351 SRP257883 Homo sapiens 10xv3 24780 Yes
    Gautam P, Hamashima K, Chen Y, ... 34584087 SRP255012 Homo sapiens 10xv2 23610 Yes
    Sridhar A, Hoshino A, Finkbein ... 32023475 SRP238587 Homo sapiens 10xv2 18575 No
    Voigt AP, Mulfaul K, Mullin NK ... 31712411 SRP218652 Homo sapiens 10xv3 12634 No
    Ligocki A, Fury W, Gutierrez C ... 34381080 SRP362101 Homo sapiens 10xv2 12289 No
    van Zyl T, Yan W, McAdams A, P ... 32341164 SRP255871 Homo sapiens 10xv2 10858 Yes
    Patel G, Fury W, Yang H, et al ... 32439707 SRP254408 Homo sapiens 10xv2 10618 No
    Lukowski SW, Lo CY, Sharov AA ... 31436334 E-MTAB-7316 Homo sapiens 10xv2 9725 Yes
    Lu Y, Shiau F, Yi W, Lu S et a ... 32386599 SRP170761 Homo sapiens 10xv2 5002 No
    Menon M, Mohammadi S, Davila-V ... 31653841 SRP222958 Homo sapiens DropSeq 3894 Yes
    Voigt AP, Whitmore SS, Flamme- ... 31075224 SRP194595 Homo sapiens 10xv3 3645 Yes
    Yan W, Peng YR, van Zyl T, Reg ... 32555229 SRP255195 Homo sapiens 10xv3 3187 No
    Swamy VS, Fufa TD, Hufnagel RB ... 34651173 SRP329495 Homo sapiens 10xv2 1544 No
    Menon M, Mohammadi S, Davila-V ... 31653841 SRP222001 Homo sapiens 10xv2 1273 Yes
    Voigt AP, Binkley E, Flamme-Wi ... 32069977 SRP238409 Homo sapiens 10xv3 1195 No
    Hu Y, Wang X, Hu B, Mao Y et a ... 31269016 SRP125998 Homo sapiens SMARTSeq_v2 8 No
    Peng YR, Shekhar K, Yan W, Her ... 30712875 SRP158528 Macaca fascicularis 10xv2 85327 Yes
    van Zyl T, Yan W, McAdams A, P ... 32341164 SRP255874 Macaca fascicularis 10xv2 4499 Yes
    Clark BS, Stein-O'Brien GL, Sh ... 31128945 SRP158081 Mus musculus 10xv2 127434 Yes
    Dani N, Herbst RH, McCabe C, G ... 33932339 SRP310237 Mus musculus 10xv2 64671 Yes
    Tabula Muris Consortium., Over ... 30283141 SRP131661 Mus musculus 10xv2 60587 Yes
    Tran NM, Shekhar K, Whitney IE ... 31784286 SRP212151 Mus musculus 10xv2 46175 Yes
    Yan W, Laboulaye MA, Tran NM, ... 32457074 SRP259930 Mus musculus 10xv2 44560 No
    Wu F, Bard JE, Kann J, Yergeau ... 33674582 SRP257758 Mus musculus 10xv2 42692 No
    Shekhar K, Lapan SW, Whitney I ... 27565351 SRP075719 Mus musculus DropSeq 24158 Yes
    Heng JS, Hackett SF, Stein-O'B ... 31843893 SRP200499 Mus musculus 10xv2 15461 No
    van Zyl T, Yan W, McAdams A, P ... 32341164 SRP251245 Mus musculus 10xv3 14827 Yes
    Macosko EZ, Basu A, Satija R, ... 26000488 SRP050054 Mus musculus DropSeq 12092 Yes
    Lehmann GL, Hanke-Gogokhia C, ... 32196081 SRP216903 Mus musculus 10xv2 9607 No
    Fadl BR, Brodie SA, Malasky M, ... 33088174 SRP269635 Mus musculus 10xv2 8516 No
    Balasubramanian R, Min X, Quin ... 34757798 SRP228556 Mus musculus 10xv3 8463 Yes
    Buenaventura DF, Corseri A, Em ... 31260032 SRP200599 Mus musculus 10xv2 8207 No
    Lo Giudice Q, Leleu M, La Mann ... 31399471 SRP168426 Mus musculus 10xv2 5040 No
    O'Koren EG, Yu C, Klingeborn M ... 30850344 SRP186407 Mus musculus 10xv2 3621 No
    Lo Giudice Q, Leleu M, La Mann ... 31399471 SRP186396 Mus musculus SMARTSeq_v2 599 No
    Clark BS, Stein-O'Brien GL, Sh ... 31128945 SRP158081 Mus musculus SMARTSeq_v2 505 No
    Fadl BR, Brodie SA, Malasky M, ... 33088174 SRP269634 Mus musculus 10xv2 358 No
    Shekhar K, Lapan SW, Whitney I ... 27565351 SRP075720 Mus musculus SMARTSeq_v2 337 No
    Shekhar K, Lapan SW, Whitney I ... 27565351 SRP073242 Mus musculus SMARTSeq_v2 246 No

    scEiaD Curated Published Cell Type Labels

    <