{ "id": 349582, "title": "Related datasets (CITE-seq, MULTIOME, etc) ", "url": "/competitions/open-problems-multimodal/discussion/349582", "author_name": "Alexander Chervov", "post_date": "2022-09-01T20:40:15.836000", "votes": 5, "comment_count": 0, "views": 0, "content": "

Let us collect here related datasets. Please add your references.

\n
    \n

  1. CITE-seq technology seems has been proposed in 2017 in paper (Cited by 1461) :
    \nStoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P . (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods
    \nThe dataset seems to be already on Kaggle
    \nhttps://www.kaggle.com/datasets/chrispr/single-cell-rna-seq-from-stoeckius-et-al-2017

    2. \n

  2. CITE-seq https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128639
    \nPlaced on Kaggle: https://www.kaggle.com/datasets/alexandervc/citeseq-scrnaseq-proteins-human-pbmcs-2019
    \nCITE-seq data on human bone marrow cells is available through GEO (accession no. GSE128639).

    3. \n

  3. CITE-seq data on PBMCs is available through 10x Genomics’ data portal (https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_protein_v3_nextgem).

    4. \n

  4. Single Cell ATAC-seq (but seems there is no ready TF-IDF format used in current competition) https://support.10xgenomics.com/single-cell-atac/datasets/1.2.0/atac_v1_pbmc_10k?
    \n10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor
    \nSingle Cell ATAC Dataset by Cell Ranger ATAC 1.2.0
    \nPeripheral blood mononuclear cells (PBMCs) from a healthy donor.
    \n~15400 transposed nuclei were loaded.
    \n8,633 nuclei were recovered.
    \nSequenced on Illumina NovaSeq with approximately 43k read pairs per cell.
    \n50bp read1, 8bp i7 (sample index), 16bp i5 (10x Barcode), 49bp read2.
    \nPublished on November 21, 2019
    \nThis dataset is licensed under the Creative Commons Attribution license.

    5. \n

  5. Datasets from the previous year similar competition:
    \n\"Multiome\"
    \nhttps://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021
    \n\"CITE-seq\"
    \nhttps://www.kaggle.com/datasets/alexandervc/citeseqscrnaseqproteins-challenge-neurips2021

    6. \n
", "messages": [ { "id": 1922931, "postDate": "2022-09-01T20:40:15.837Z", "content": "

Let us collect here related datasets. Please add your references.

\n
    \n

  1. CITE-seq technology seems has been proposed in 2017 in paper (Cited by 1461) :
    \nStoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P . (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods
    \nThe dataset seems to be already on Kaggle
    \nhttps://www.kaggle.com/datasets/chrispr/single-cell-rna-seq-from-stoeckius-et-al-2017

    2. \n

  2. CITE-seq https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128639
    \nPlaced on Kaggle: https://www.kaggle.com/datasets/alexandervc/citeseq-scrnaseq-proteins-human-pbmcs-2019
    \nCITE-seq data on human bone marrow cells is available through GEO (accession no. GSE128639).

    3. \n

  3. CITE-seq data on PBMCs is available through 10x Genomics’ data portal (https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_protein_v3_nextgem).

    4. \n

  4. Single Cell ATAC-seq (but seems there is no ready TF-IDF format used in current competition) https://support.10xgenomics.com/single-cell-atac/datasets/1.2.0/atac_v1_pbmc_10k?
    \n10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor
    \nSingle Cell ATAC Dataset by Cell Ranger ATAC 1.2.0
    \nPeripheral blood mononuclear cells (PBMCs) from a healthy donor.
    \n~15400 transposed nuclei were loaded.
    \n8,633 nuclei were recovered.
    \nSequenced on Illumina NovaSeq with approximately 43k read pairs per cell.
    \n50bp read1, 8bp i7 (sample index), 16bp i5 (10x Barcode), 49bp read2.
    \nPublished on November 21, 2019
    \nThis dataset is licensed under the Creative Commons Attribution license.

    5. \n

  5. Datasets from the previous year similar competition:
    \n\"Multiome\"
    \nhttps://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021
    \n\"CITE-seq\"
    \nhttps://www.kaggle.com/datasets/alexandervc/citeseqscrnaseqproteins-challenge-neurips2021

    6. \n
", "rawMarkdown": "Let us collect here related datasets. Please add your references. \n\n\n1. **CITE-seq technology seems has been proposed in 2017 in paper (Cited by 1461) :**\nStoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P . (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods \nThe dataset seems to be already on Kaggle\nhttps://www.kaggle.com/datasets/chrispr/single-cell-rna-seq-from-stoeckius-et-al-2017\n\n2. CITE-seq https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128639\nPlaced on Kaggle: https://www.kaggle.com/datasets/alexandervc/citeseq-scrnaseq-proteins-human-pbmcs-2019\nCITE-seq data on human bone marrow cells is available through GEO (accession no. GSE128639). \n\n3. CITE-seq data on PBMCs is available through 10x Genomics’ data portal (https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_protein_v3_nextgem).\n\n\n4. Single Cell ATAC-seq (but seems there is no ready TF-IDF format used in current competition) https://support.10xgenomics.com/single-cell-atac/datasets/1.2.0/atac_v1_pbmc_10k?\n10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor\nSingle Cell ATAC Dataset by Cell Ranger ATAC 1.2.0\nPeripheral blood mononuclear cells (PBMCs) from a healthy donor.\n~15400 transposed nuclei were loaded.\n8,633 nuclei were recovered.\nSequenced on Illumina NovaSeq with approximately 43k read pairs per cell.\n50bp read1, 8bp i7 (sample index), 16bp i5 (10x Barcode), 49bp read2.\nPublished on November 21, 2019\nThis dataset is licensed under the Creative Commons Attribution license.\n\n5. Datasets from the previous year similar competition:\n\"Multiome\"\nhttps://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021\n\"CITE-seq\"\nhttps://www.kaggle.com/datasets/alexandervc/citeseqscrnaseqproteins-challenge-neurips2021\n\n", "votes": 5 } ], "comments": [], "raw_markdown_by_id": { "1922931": "Let us collect here related datasets. Please add your references. \n\n\n1. **CITE-seq technology seems has been proposed in 2017 in paper (Cited by 1461) :**\nStoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P . (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods \nThe dataset seems to be already on Kaggle\nhttps://www.kaggle.com/datasets/chrispr/single-cell-rna-seq-from-stoeckius-et-al-2017\n\n2. CITE-seq https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128639\nPlaced on Kaggle: https://www.kaggle.com/datasets/alexandervc/citeseq-scrnaseq-proteins-human-pbmcs-2019\nCITE-seq data on human bone marrow cells is available through GEO (accession no. GSE128639). \n\n3. CITE-seq data on PBMCs is available through 10x Genomics’ data portal (https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_protein_v3_nextgem).\n\n\n4. Single Cell ATAC-seq (but seems there is no ready TF-IDF format used in current competition) https://support.10xgenomics.com/single-cell-atac/datasets/1.2.0/atac_v1_pbmc_10k?\n10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor\nSingle Cell ATAC Dataset by Cell Ranger ATAC 1.2.0\nPeripheral blood mononuclear cells (PBMCs) from a healthy donor.\n~15400 transposed nuclei were loaded.\n8,633 nuclei were recovered.\nSequenced on Illumina NovaSeq with approximately 43k read pairs per cell.\n50bp read1, 8bp i7 (sample index), 16bp i5 (10x Barcode), 49bp read2.\nPublished on November 21, 2019\nThis dataset is licensed under the Creative Commons Attribution license.\n\n5. Datasets from the previous year similar competition:\n\"Multiome\"\nhttps://www.kaggle.com/datasets/alexandervc/scrnaseq-scatacseq-challenge-at-neurips-2021\n\"CITE-seq\"\nhttps://www.kaggle.com/datasets/alexandervc/citeseqscrnaseqproteins-challenge-neurips2021\n\n" } }