UMAP

Five nearest cells (click image):


Download Scatter Plot
Five nearest cells (click image):

Download Meta Plot

Tables













Expression Plot

Download Scatter Plot


































































































































































In Situ Projection


























































































































Facet UMAP

Download Scatter Plot










































































Dotplot

Download Dot Plot

























































































































Gene Differential Expression Tests

Tests run with scran findMarkers tool. The findMarkers tool uses the wilcox test, with the study used as the covariate, to generate the AUC scores and p values (the wilcox test is a bit more conservative than the t-test and more robust when outliers are present). The log Fold Change (logFC) scores are generated by findMarkers t-test method.

What is "AUC"? Area Under the Curve. This reports the power of the gene of interest to distinguish between the base group (e.g. Rods) versus the comparison group (e.g. Cones). An AUC of 1 mean thats the marker can perfectly (100%) distinguish cells between the two groups with the marker. AUC of 0 (or missing) means that the gene has no power.


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)

    • Diff Testing Results

  • All Diff Results (~1.1GB)
  • CellType (Predict) against CellType testing
  • CellType against CellType (Predict)
  • Cluster against Cluster

    • Metadata

  • Cell Metadata

    • Counts

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


















    • plae v0.90


    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 42 studies, 31 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 80681 Yes
    Ghinia Tegla MG, Buenaventura ... 32347797 SRP238072 Gallus gallus 10xv2 5111 No
    Collin J, Queen R, Zerti D, Bo ... 33865984 SRP275814 Homo sapiens 10xv2 78435 Yes
    He S, Wang LH, Liu Y, Li YQ et ... 33287869 SRP292721 Homo sapiens 10xv2 68360 Yes
    Lu Y, Shiau F, Yi W, Lu S et a ... 32386599 SRP223254 Homo sapiens 10xv2 51171 Yes
    Cowan CS, Renner M, De Gennaro ... 32946783 EGAD00001006350 Homo sapiens 10xv2 50050 Yes
    Lu Y, Shiau F, Yi W, Lu S et a ... 32386599 SRP151023 Homo sapiens 10xv2 39028 Yes
    Yan W, Peng YR, van Zyl T, Reg ... 32555229 SRP255195 Homo sapiens 10xv2 25936 Yes
    Voigt AP, Whitmore SS, Mulfaul ... 32531351 SRP257883 Homo sapiens 10xv3 24828 Yes
    Sridhar A, Hoshino A, Finkbein ... 32023475 SRP238587 Homo sapiens 10xv2 13102 No
    Voigt AP, Mulfaul K, Mullin NK ... 31712411 SRP218652 Homo sapiens 10xv3 12582 Yes
    van Zyl T, Yan W, McAdams A, P ... 32341164 SRP255871 Homo sapiens 10xv2 10701 Yes
    Patel G, Fury W, Yang H, et al ... 32439707 SRP254408 Homo sapiens 10xv2 8773 No
    Lukowski SW, Lo CY, Sharov AA ... 31436334 E-MTAB-7316 Homo sapiens 10xv2 8580 Yes
    Lu Y, Shiau F, Yi W, Lu S et a ... 32386599 SRP170761 Homo sapiens 10xv2 4806 No
    Voigt AP, Whitmore SS, Flamme- ... 31075224 SRP194595 Homo sapiens 10xv3 3697 Yes
    Menon M, Mohammadi S, Davila-V ... 31653841 SRP222958 Homo sapiens DropSeq 3013 Yes
    Yan W, Peng YR, van Zyl T, Reg ... 32555229 SRP255195 Homo sapiens 10xv3 2849 Yes
    Swamy VS, Fufa TD, Hufnagel RB ... 34651173 SRP329495 Homo sapiens 10xv2 1523 Yes
    Voigt AP, Binkley E, Flamme-Wi ... 32069977 SRP238409 Homo sapiens 10xv3 1299 No
    Menon M, Mohammadi S, Davila-V ... 31653841 SRP222001 Homo sapiens 10xv2 1103 Yes
    Hu Y, Wang X, Hu B, Mao Y et a ... 31269016 SRP125998 Homo sapiens SMARTSeq_v2 11 No
    Peng YR, Shekhar K, Yan W, Her ... 30712875 SRP158528 Macaca fascicularis 10xv2 71927 Yes
    van Zyl T, Yan W, McAdams A, P ... 32341164 SRP255874 Macaca fascicularis 10xv2 4398 Yes
    Clark BS, Stein-O'Brien GL, Sh ... 31128945 SRP158081 Mus musculus 10xv2 135131 Yes
    Dani N, Herbst RH, McCabe C, G ... 33932339 SRP310237 Mus musculus 10xv2 83506 Yes
    Tabula Muris Consortium., Over ... 30283141 SRP131661 Mus musculus 10xv2 63072 Yes
    Tran NM, Shekhar K, Whitney IE ... 31784286 SRP212151 Mus musculus 10xv2 48002 Yes
    Yan W, Laboulaye MA, Tran NM, ... 32457074 SRP259930 Mus musculus 10xv2 45502 Yes
    Wu F, Bard JE, Kann J, Yergeau ... 33674582 SRP257758 Mus musculus 10xv2 43750 No
    Shekhar K, Lapan SW, Whitney I ... 27565351 SRP075719 Mus musculus DropSeq 27342 Yes
    Heng JS, Hackett SF, Stein-O'B ... 31843893 SRP200499 Mus musculus 10xv2 18553 Yes
    van Zyl T, Yan W, McAdams A, P ... 32341164 SRP251245 Mus musculus 10xv3 14895 Yes
    Macosko EZ, Basu A, Satija R, ... 26000488 SRP050054 Mus musculus DropSeq 12121 Yes
    Lehmann GL, Hanke-Gogokhia C, ... 32196081 SRP216903 Mus musculus 10xv2 10046 No
    Fadl BR, Brodie SA, Malasky M, ... 33088174 SRP269635 Mus musculus 10xv2 9023 No
    Buenaventura DF, Corseri A, Em ... 31260032 SRP200599 Mus musculus 10xv2 8208 No
    Lo Giudice Q, Leleu M, La Mann ... 31399471 SRP168426 Mus musculus 10xv2 5257 No
    O'Koren EG, Yu C, Klingeborn M ... 30850344 SRP186407 Mus musculus 10xv2 3631 No
    Lo Giudice Q, Leleu M, La Mann ... 31399471 SRP186396 Mus musculus SMARTSeq_v2 798 No
    Clark BS, Stein-O'Brien GL, Sh ... 31128945 SRP158081 Mus musculus SMARTSeq_v2 779 No
    Fadl BR, Brodie SA, Malasky M, ... 33088174 SRP269634 Mus musculus 10xv2 365 No
    Shekhar K, Lapan SW, Whitney I ... 27565351 SRP075720 Mus musculus SMARTSeq_v2 351 No
    Shekhar K, Lapan SW, Whitney I ... 27565351 SRP073242 Mus musculus SMARTSeq_v2 254 No

    scEiaD Curated Published Cell Type Labels

    CellType Species Studies Count
    Amacrine Cell GG, HS, MF, MM 11 79936
    Retinal Ganglion Cell GG, HS, MF, MM 12 72977
    Rod GG, HS, MF, MM 12 49998
    Epithelial HS, MM 4 48332
    Fibroblast HS, MM 7 44715
    Muller Glia GG, HS, MF, MM 14 39676
    Bipolar Cell GG, HS, MF, MM 13 38512
    Keratocyte HS 1 31675
    Early RPC MM 1 26711
    T/NK-Cell HS, MF, MM 7 26263
    Late RPC MM 1 17686
    B-Cell HS, MM 5 16929
    RPC HS 2 16072
    Endothelial HS, MF, MM 14 11662
    Cone GG, HS, MF, MM 8 11105
    Rod Bipolar Cell HS, MM 3 10687
    Neurogenic Cell HS, MM 3 8408
    Macrophage HS, MF, MM 8 8195
    Mesenchymal MM 1 7835
    Horizontal Cell GG, HS, MF, MM 9 7301
    Keratinocyte HS, MM 2 6256
    Pericyte HS, MF, MM 8 6167
    Photoreceptor Precursor HS, MM 3 6003
    Basal Cell HS, MM 2 5720
    Beam HS, MF, MM 3 4798
    Schwann HS, MF 4 4560
    Melanocyte HS, MF, MM 8 4482
    Blood Vessel HS 2 4215
    Smooth Muscle Cell HS 2 4120
    Neural Crest HS 1 3199
    Limbal HS 1 2807
    Uveal MM 1 2578
    Corneal Progenitor HS 1 2476
    Monocyte HS, MM 3 2432
    Corneal Epithelial HS, MM 3 2205
    Red Blood Cell HS, MM 4 2178
    Satellite Cell HS 1 2155
    Proliferating Cornea HS 1 1963
    Ciliary Muscle HS, MF 2 1547
    Plasma Cell HS 1 1528
    Amacrine/Horizontal Precursorsor HS 2 1338
    Hepatocyte MM 1 1327
    Enterocyte HS 1 1207
    Bladder MM 1 1191
    Astrocyte HS 2 1175
    Bladder Urothelial MM 1 1154
    Mesenchymal (Stem) MM 1 1129
    Choriocapillaris HS 1 893
    Corneal Endothelial HS 1 846
    Microglia HS, MF 6 833
    JCT HS, MF, MM 3 817
    Mesoderm HS 1 815
    Conjunctival Epithelial HS 1 630
    Kidney Proximal Tubule MM 1 419
    Cholangiocyte HS 1 325
    Corneal Basement Membrane HS 1 310
    Corneal Nerve HS 1 229
    Ciliary Margin HS 1 228
    Limbal Progenitor HS 1 197
    Secretory Cell HS 1 194
    Oligodendrocyte GG 1 162
    Mast HS 1 141
    Schlemm's Canal MF 1 117
    Meningeal MM 1 108
    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 20 166076
    Retinal Ganglion Cell GG, HS, MF, MM 18 106805
    Rod GG, HS, MF, MM 21 95599
    Epithelial HS, MM 4 70808
    Fibroblast HS, MF, MM 15 69050
    Bipolar Cell GG, HS, MF, MM 25 68927
    Muller Glia GG, HS, MF, MM 21 57928
    Late RPC HS, MM 6 37845
    Early RPC HS, MM 7 35675
    Keratocyte HS, MM 4 33408
    T/NK-Cell HS, MF, MM 11 30425
    RPC HS, MM 6 28803
    Cone GG, HS, MF, MM 20 26916
    B-Cell HS, MM 9 22806
    Endothelial HS, MF, MM 20 17055
    Photoreceptor Precursor HS, MM 4 16221
    Rod Bipolar Cell HS, MM 7 13420
    Horizontal Cell GG, HS, MF, MM 16 12969
    Neurogenic Cell HS, MM 4 12389
    Pericyte HS, MF, MM 16 12070
    Macrophage HS, MF, MM 12 11693
    Corneal Epithelial HS, MM 3 10797
    Schwann HS, MF, MM 11 9560
    Beam HS, MF, MM 5 9064
    Mesenchymal MM 1 8152
    Keratinocyte HS, MM 2 6592
    RPE HS 4 6274
    Neural Crest HS 1 6217
    Basal Cell HS, MM 2 6131
    Blood Vessel HS 5 5481
    Melanocyte HS, MF, MM 10 5399
    Smooth Muscle Cell HS 3 4556
    Limbal HS 1 4492
    Monocyte HS, MM 4 3078
    Corneal Progenitor HS, MM 3 2979
    Satellite Cell HS 1 2879
    Red Blood Cell HS, MM 5 2661
    Uveal MM 1 2651
    Hepatocyte MM 1 2365
    Amacrine/Horizontal Precursorsor HS, MM 5 2005
    Proliferating Cornea HS 1 1975
    Plasma Cell HS 1 1867
    Ciliary Muscle HS, MF 3 1833
    Oligodendrocyte GG, MM 3 1670
    Astrocyte HS 4 1514
    Mesenchymal (Stem) MM 1 1396
    Enterocyte HS 1 1313
    Bladder MM 1 1295
    Conjunctival Epithelial HS, MM 2 1285
    Microglia HS, MF, MM 10 1270
    Bladder Urothelial MM 1 1173
    Corneal Endothelial HS 3 500
    Kidney Proximal Tubule MM 1 433
    Cholangiocyte HS 1 426
    Corneal Nerve HS 1 227
    JCT MM 1 150
    Secretory Cell HS 1 99
    Limbal Progenitor HS 1 76
    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
    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

    0.90 (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