UMAP

Five nearest cells (click image):

setSliderColor has been deprecated and will be removed in a future release of shinyWidgets. If you absolutely need to use this function, copy the source code into your project https://github.com/dreamRs/shinyWidgets/blob/26838f9e9ccdc90a47178b45318d110f5812d6e1/R/setSliderColor.R
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 v0.94


    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

    CellType Species Studies Count
    Retinal Ganglion Cell GG, HS, MF, MM 11 68030
    Rod GG, HS, MF, MM 11 50622
    Fibroblast HS, MM 7 45697
    Amacrine Cell GG, HS, MF, MM 8 42005
    Epithelial HS, MM 4 35426
    Muller Glia GG, HS, MF, MM 13 34821
    Bipolar Cell GG, HS, MF, MM 12 33777
    Keratocyte HS 1 31850
    T/NK-Cell HS, MF, MM 7 29486
    Early RPC MM 1 25441
    RPC HS, MM 3 20899
    Late RPC MM 1 17645
    B-Cell HS, MM 4 16194
    Endothelial HS, MF, MM 12 12028
    Neurogenic Cell HS, MM 4 9219
    Rod Bipolar Cell HS, MM 2 9063
    Horizontal Cell GG, HS, MF, MM 7 8152
    Cone GG, HS, MF, MM 8 8040
    Mesenchymal MM 1 7840
    Macrophage HS, MF, MM 6 7190
    Photoreceptor Precursor HS, MM 4 6643
    Keratinocyte HS, MM 2 6272
    Melanocyte HS, MF, MM 8 6143
    Pericyte HS, MF, MM 7 5470
    Basal Cell HS, MM 2 5153
    Beam HS, MF, MM 3 4955
    Proliferating Cornea HS 1 4184
    Blood Vessel HS 2 4126
    Neural Crest HS 1 4030
    Smooth Muscle Cell HS 2 4005
    Schwann HS, MF 4 3225
    Monocyte HS, MM 4 2654
    Uveal MM 1 2578
    Red Blood Cell HS, MM 4 2507
    Satellite Cell HS 1 2295
    Corneal Progenitor HS 1 2243
    Corneal Epithelial HS, MM 3 2133
    Limbal HS 1 2040
    AC/HC Precursor HS, MM 3 1812
    Ciliary Margin HS, MM 2 1777
    Enterocyte HS 1 1725
    Plasma Cell HS 1 1591
    Ciliary Body HS 1 1476
    Ciliary Muscle HS, MF 2 1373
    Bladder MM 1 1191
    Bladder Urothelial MM 1 1154
    Mesenchymal (Stem) MM 1 1129
    Hepatocyte MM 1 1016
    Conjunctival Epithelial HS 2 914
    Corneal Endothelial HS 1 906
    Choriocapillaris HS 1 892
    JCT HS, MF, MM 3 821
    Mesoderm HS 1 718
    Microglia HS, MF 6 641
    Secretory Cell HS 1 352
    Corneal Basement Membrane HS 1 316
    Astrocyte HS 1 277
    Cholangiocyte HS 1 244
    Corneal Nerve HS 1 227
    Schlemm's Canal MF 1 119
    Limbal Progenitor HS 1 116
    Oligodendrocyte GG 1 111
    Meningeal MM 1 60
    Kidney Proximal Tubule MM 1 57
    Labelled cell types from published papers were pulled, where possible, from a combination of the Sequence Read Archive (SRA), lab web sites, and personal correspondence, then adjusted to be consistent (e.g. MG to Muller Glia) between all studies. Only cell type - study combinations with >50 cells were included in this table.


    scEiaD Machine Learned Cell Type Labels

    CellType Species Studies Count
    Amacrine Cell GG, HS, MF, MM 21 134443
    Rod GG, HS, MF, MM 22 119666
    Retinal Ganglion Cell GG, HS, MF, MM 21 107407
    Fibroblast HS, MM 16 74758
    Bipolar Cell GG, HS, MF, MM 25 67880
    Muller Glia GG, HS, MF, MM 20 62042
    Epithelial HS, MM 7 55571
    RPC HS, MM 9 43465
    Keratocyte HS 2 36650
    Late RPC HS, MM 7 34663
    T/NK-Cell HS, MF, MM 10 33876
    Early RPC HS, MM 10 32412
    Neural Crest HS 1 25239
    Corneal Epithelial HS, MM 5 22814
    Cone GG, HS, MF, MM 20 21394
    Endothelial HS, MF, MM 22 20744
    B-Cell HS, MM 8 19994
    Neurogenic Cell HS, MM 8 17785
    Photoreceptor Precursor HS, MM 5 14770
    Horizontal Cell GG, HS, MF, MM 15 14092
    Pericyte HS, MF, MM 16 11312
    Rod Bipolar Cell HS, MM 5 10331
    Blood Vessel HS, MM 5 10245
    Macrophage HS, MF, MM 12 10042
    Schwann HS, MF, MM 7 9366
    Mesenchymal MM 1 8054
    Melanocyte HS, MF, MM 12 8030
    Keratinocyte HS, MM 2 6635
    Smooth Muscle Cell HS, MM 4 6172
    Beam HS, MF, MM 5 5741
    Microglia HS, MF, MM 17 5674
    RPE HS, MM 6 5583
    Basal Cell HS, MM 2 5474
    Proliferating Cornea HS 1 4905
    Conjunctival Epithelial HS, MM 4 4467
    AC/HC Precursor HS, MM 6 4223
    Monocyte HS, MM 6 3233
    Satellite Cell HS 1 3126
    Red Blood Cell HS, MM 6 2991
    Uveal HS, MM 2 2860
    Corneal Progenitor HS, MM 5 2678
    Plasma Cell HS 1 1962
    Enterocyte HS 1 1877
    Ciliary Muscle HS, MF, MM 4 1756
    Limbal HS 2 1690
    Ciliary Body HS 1 1595
    Mesenchymal (Stem) MM 1 1395
    Bladder MM 1 1308
    Hepatocyte MM 1 1294
    Astrocyte HS 2 1183
    Bladder Urothelial MM 1 1165
    Ciliary Margin HS, MM 3 963
    JCT HS, MF 3 723
    Corneal Endothelial HS 2 664
    Corneal Nerve HS 2 374
    Secretory Cell HS 1 340
    Cholangiocyte HS 1 327
    Kidney Proximal Tubule MM 1 72
    Limbal Progenitor HS 1 60
    The labels above were used to create a machine learning modeled which was used to relabel all* cells in the scEiaD (*above a confidence threshold of 0.5). Only cell type - study combinations with >50 cells were included in this table.


    Using and Extending plae and the scEiaD


    All Links are External

    Analyses Colab Bash Web Guide
    Using plae Go
    Custom plotting Go
    Using scEiaD Seurat object Go
    UMAP projection of your data on scEiaD Go
    Auto cell type label your data Go Go

    Contact

    If you have questions about scEiaD dataset or the plae application, please contact David McGaughey, Ph.D .

    Otherwise the National Eye Institute's Office of Science Communications, Public Liaison and Education responds directly to requests for information on eye diseases and vision research in English and Spanish. We cannot provide personalized medical advice to individuals about their condition or treatment.

    2020@nei.nih.gov
    Phone: 301-496-5248 — English and Spanish
    Mail: National Eye Institute
    Information Office
    31 Center Drive MSC 2510
    Bethesda, MD 20892-2510








































    Change log

    0.94 (2023-06-12): Added error handling for exp plot situation where user filters all the data out. Fixed bug in logic for exp plot category filtering

    0.93 (2023-02-16): Added the ability to download the plot data for the UMAPs, Expression Plot, Dot Plot, and Heatmap. New quick analysis document added here which gives a quick workthrough about how to recreate the plot locally in R. Enhanced the "Pop Up" buttons with more information and GIFs showing some functionality of the app.

    0.92 (2022-08-30): Default in Exp Plot now facets on Gene. Exp Plot facet_wrap now on "free_y" instead of "free" to save space.

    0.91 (2022-07-30): Updated diff testing model to a pseudoBulk / DESeq2 based approach. Now run on a per-species basis. Added heatmap visualization which uses the DESeq2 pseudobulk differential experession changes. Fixed bug that removed RPC from the Expression Plot view as well as display zero expression studies. Expression Plot view has a new option which flips flops the facet / x axis from Gene to whatever the cell type label is (CellType, CellType_predict, cluster).

    0.90 (2022-04-05): Updated scEiaD scVI model once more. We simplified pan species gene name alignment by removing "one to many" or "many to one" name alignments. All expressed genes are still retained in the individual species (if not present in the gene name name merged matrix), but we are less aggressive about merging gene names across species as we discovered some edge cases where many genes were getting merged into one and vice versa. This necessitated a new core gene <-> cell count matrix and thus a new scVI model. We also took the opportunity to add a new mouse retina development dataset from Balasubramanian et al. and a new ocular compartment dataset from Gautam et al. We leverage the new(ish) scANVI scVI approach in which we use the community cell type labels to subtly improve the scVI modelling of the cell types. We hope this is the final update (hah) before we submit this work.

    0.85 (2022-02-15): Updated example analysis to match current data, fixed 508/a11y compliance issues with the document. Added *study level* seurat and anndata objects to "Data". Updated the large downloadable Seurat / anndata objects with intronic counts data (velocity?!). Added "Other Resources" section for alternative resources for ocular transcriptomics.

    0.84 (2021-11-17): Added large human cornea dataset from Collins et al. As this was a brand new human dataset with several new cell types, a new scVI model was built. Hence the new apperance of the UMAP view. Fixed a filtering bug in Exp Plot.

    0.83 (2021-11-01): Fixed bug in metadata filter table loading that was messing up some of the plots in certain situations. Tweak click table column choice to include organism.

    0.82 (2021-10-26): Cool feature! Now you can click the UMAP viz to get cell info!

    0.81 (2021-10-25): Fix small bug in bindCache logic, improve exp plot plotting by retaining zero expression studies

    0.80 (2021-10-22): MASSIVE update. Chicken data added. Brain choroid added. Trabecular meshword added. Cornea added. Human body tissues added. We now have over one million cells in this resource. Counts cleaned up with DecontX to remove (mostly) Rod gene contamination (e.g. Rho *was* everywhere). singleCellHaystack table added to Diff Testing. Updated cell filtering with a higher minimum gene count cutoff to improve overall quality. Fixed bug in gene selection where human genes that mapped to multiple mouse genes were accidently removed. Improved scran-based differential gene expression testing with better parameters and added logFC calculations to improve interpretability. Fixed bug in dotplot plot where filtered data had the incorrect denominator values.

    0.74 (2021-08-12): Remove broken link, fix bug in Expression Plot that was making average expression far too low.

    0.73 (2021-06-10): Allow for UMAP plot to show all values (filter now starts at >=1)

    0.72 (2021-04-29): Added more content and a table organzation to the Info -> Analysis... section

    0.71 (2021-04-14): scEiaD preprint on bioRxiv! Added a filter in the "exp plot" section to remove data points with user-selected (default 50) minimum cells. I was finding that data points (e.g. cell type - study) with low N would often have "outlier" results. Added a new section to the web page - Analyses (under the "Info" tab)!

    0.70 (2021-03-22): New scEiaD built with corrected fastq file sets (potential bug in 10x bamtofastq tool resulted ina few datasets getting scrambled barcodes). Removed a macaque dataset (SRR7733526) with odd behavior (clustering in 2D UMAP space largely alone). Tweaked the UMAP 2D gene view with a darker "background" cell color scheme to reduce "over-emphasis" on cells with low expression of a gene. CPM replaced with counts as some odd behaviour was detected in some genes in the UMAP view where there was high "background" expression. Counts have more consistent behavior. Removed hard filter that tossed cells with >2500 detected genes.

    0.60 (2021-02-08): New scEiaD built with more studies. Removed several retinal organoid datasets that had snuck in. Added a filter option for the diff searching to search, for example, one cluster directly against another cluster.

    0.52 (2021-01-13): Adding "missing" genes (we had only retained genes which were expressed in all three species, which naturally led to many genes (some important, like OPN1MW) to be dropped. That has been fixed. Tweak "Expression Plot" dot size to prevent crazy tiny point sizes.

    0.51 (2021-01-06): Hello 2021! Adam Gayoso kindly pointed out that I was using scVI in a non-optimal manner, so I updated the scVI modeling to match their recommend "scArches" parameters. This (fortunately for my sanity) only subtly changes the downstream result. The more significant change is that we have totally changed the diff testing section to use the scran findMarkers test instead of our complicated and compute expensive pseudo-Bulk testing which continually gave odd results.

    0.50 (2020-12-31): Goodbye 2020! Major update to the scVI-based UMAP projection which improves data quality. Removed non-tissue samples (e.g. organoid/cell lines). They will be added back later once I figure out a logical/simple way to do it. Fixed major bug in QC filtering which failed to remove high mitochondrial count (likely apoptosing cells) cells. Dot plot tweaked to improve relative dot sizes. Cowan et al. dataset added.

    0.43 (2020-11-09): Downloadable diff results added to "Data." The diff results reactive data table now has a "Download all ..." button which replaces the "CSV" button that only downloaded the viewable data (100 max).

    0.42 (2020-10-16): Alt text added to each button, tweaked UMAP-Tables layout again. Slide logo added. Site went public at the version on 2020-11-02!

    0.41 (2020-10-06): Download buttons added for each plot.

    0.40 (2020-10-05): UI and text labels tweaked in UMAP-Tables to improve tab selection order. Dot plot given a bar plot to show category size. Error handling improved when user fails to provide a category value to filter on. Data Table help button added. Help buttons moved to bottom of page with consistent visual - tabbing order. Colors of UI elements tweaked to improve contrast.

    0.39 (2020-09-18): Fixed calculation error in dotplot where expression not scaled by number of cells in grouping variable.

    0.38 (2020-09-02): Contact section and footer added for compliance.

    0.37 (2020-08-24): Help pop section populated with text. Put white halo back around text in UMAP - Meta section. Loading circles added to plots. Row names added to tables. Diff testing filtered to only return results with FDR < 0.05 and abs(logFC) > 0.5.

    0.36 (2020-08-17): Data download section added. Change log moved to separate section. CSS tweaked to show links in blue. First overview table updated to improve contrast. UMAP plots axis fixed.

    0.35 (2020-08-14): Moved Overview tables to html for improved rendering and switched over color-blind friendly palette. Temporarily removed Temporal Plotting section. Improved filtering for Facet Plot. DotPlot plotting fixed and improved. Back-end server.R code moved into separate functions. Colors fixed so they stay consistent when filtering/subsetting the plots. Site now starts from scratch in under 5 seconds with improved fst-based data loading and pre-calculating more operations.

    0.34 (2020-08-03): Fixed issue with TabulaMuris labels not appearing. Scanned app with koa11y for 508 compliance - changed headers from h2 to h1 to comply.

    0.33 (2020-07-30): Exp plot can now take space or comma separated Genes as input. User can selected number of columns in Exp Plot. Diff Table formatting improved with rounding and PB_Test can be selected as a drop down now in the data table search.

    0.32 (2020-07-29): In situ Projection viz added courtesy of Zachary Batz! It's a simulated cross section of the retina with each cell type colored by intensity of scRNA expression! Move table draw button under filtering in UMAP - Tables. Sort Diff Exp results by FDR. Filtering on numeric column now returns slider UI. Remove super dangerous ability to create faceted plots on numeric values.

    0.31 (2020-07-24): Re-created scEiaD with better internal (Hufnagel) transwell RPE labelling (there are roughly two groups - mature RPE with high TTR expression and less (?) mature RPE with lower TTR), removal of the SRP166660 study as it was *all* non-normal (injured retina) (confirmed with correspondence with Dr. Poche), removed the pan RGC CellType labelling for the SRP212151 as I see post-hoc that there are LOADS of non-RGC cells. Did the same for SRP186407, which has substantial non-microglia. Generally, FACS != 100% celltype purity. Added differential testing against all Tabula Muris cell types. Removing clusters/cells with high doublet scores. Added cell cycle phase (G1/G2M/S) assignment. More study level metadata.

    0.30 (2020-07-20): Huge update. Hundreds of thousands of cells added. The Tabula Muris project data (pan mouse) has been added to faciliate non-eye comparison. Filtering options added to most of the plotting views to allow for quick slicing into this huge dataset. Differential expression testing totally reworked - now uses "pseudoBulk" approach to better utilize the large number of studies we have.

    0.23 (2020-06-16): Remove low N cell type from diff expression tables, tweak Overview with spacing alterations and updated text.

    0.22 (2020-06-15): Added expression plot by user selected groups plot view. Fixed bug in mean cpm expression calculation for Viz -> UMAP - Table gene tables

    0.21 (2020-06-15): Added subcluster diff testing tables, temporal gene expression by celltype plot section.

    0.20 (2020-06-06): New 2D UMAP projection that includes the full Yu - Clark Human scRNA dataset. Added tables to "Overview" section showing data stats. Added "filtering" functionality to UMAP plot section.


    Other potentially useful resources for single cell ocular trancriptomics
    Independent datasets : uses data from multiple independent groups
    Harmonization datasets : integrates data from multiple resources with consistent bioinformatic tooling