Workflows

These workflows are associated with Filter, plot and explore single-cell RNA-seq data with Scanpy

To use these workflows in Galaxy you can either click the links to download the workflows, or you can right-click and copy the link to the workflow which can be used in the Galaxy form to import workflows.

CS3_Filter, Plot and Explore Single-cell RNA-seq Data

Last updated Nov 18, 2022

Launch in Tutorial Mode question
License: None Specified, defaults to CC-BY-4.0
Tests: ❌ Results: Not yet automated

flowchart TD
  0["ℹ️ Input Dataset\nMito-counted AnnData"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["Inspect AnnData"];
  0 -->|output| 1;
  2["Scanpy FilterCells"];
  0 -->|output| 2;
  08175d7f-f4f4-413c-ad86-f59587ff692e["Output\nGenes-filtered Object"];
  2 --> 08175d7f-f4f4-413c-ad86-f59587ff692e;
  style 08175d7f-f4f4-413c-ad86-f59587ff692e stroke:#2c3143,stroke-width:4px;
  3["Plot"];
  0 -->|output| 3;
  bfc499ee-630a-498b-9be6-696d9bb78837["Output\nScatter - genes x UMIs"];
  3 --> bfc499ee-630a-498b-9be6-696d9bb78837;
  style bfc499ee-630a-498b-9be6-696d9bb78837 stroke:#2c3143,stroke-width:4px;
  4["Plot"];
  0 -->|output| 4;
  f2c78ef3-7d31-4930-954c-0133cce27a41["Output\nScatter - mito x genes"];
  4 --> f2c78ef3-7d31-4930-954c-0133cce27a41;
  style f2c78ef3-7d31-4930-954c-0133cce27a41 stroke:#2c3143,stroke-width:4px;
  5["Plot"];
  0 -->|output| 5;
  348818e6-9def-41fd-a390-6f8525c57cd8["Output\nViolin - genotype - log"];
  5 --> 348818e6-9def-41fd-a390-6f8525c57cd8;
  style 348818e6-9def-41fd-a390-6f8525c57cd8 stroke:#2c3143,stroke-width:4px;
  6["Plot"];
  0 -->|output| 6;
  844f5e7c-78c8-4f28-8e15-cea35ada8fef["Output\nViolin - batch - log"];
  6 --> 844f5e7c-78c8-4f28-8e15-cea35ada8fef;
  style 844f5e7c-78c8-4f28-8e15-cea35ada8fef stroke:#2c3143,stroke-width:4px;
  7["Inspect AnnData"];
  0 -->|output| 7;
  8["Plot"];
  0 -->|output| 8;
  3b1232c5-d16a-434d-806e-79fd77f7c05f["Output\nScatter - mito x UMIs"];
  8 --> 3b1232c5-d16a-434d-806e-79fd77f7c05f;
  style 3b1232c5-d16a-434d-806e-79fd77f7c05f stroke:#2c3143,stroke-width:4px;
  9["Inspect AnnData"];
  0 -->|output| 9;
  10["Plot"];
  0 -->|output| 10;
  a639cdc0-da40-4df9-8348-23117528b24a["Output\nViolin - sex - log"];
  10 --> a639cdc0-da40-4df9-8348-23117528b24a;
  style a639cdc0-da40-4df9-8348-23117528b24a stroke:#2c3143,stroke-width:4px;
  11["Plot"];
  2 -->|output_h5ad| 11;
  69bf3e42-63e2-4b5b-9d63-5aac9d6b5691["Output\nViolin - Filterbygenes"];
  11 --> 69bf3e42-63e2-4b5b-9d63-5aac9d6b5691;
  style 69bf3e42-63e2-4b5b-9d63-5aac9d6b5691 stroke:#2c3143,stroke-width:4px;
  12["Scanpy FilterCells"];
  2 -->|output_h5ad| 12;
  f378cb4e-0a77-49d9-b92a-752dbea4b09a["Output\nCounts-filtered Object"];
  12 --> f378cb4e-0a77-49d9-b92a-752dbea4b09a;
  style f378cb4e-0a77-49d9-b92a-752dbea4b09a stroke:#2c3143,stroke-width:4px;
  13["Inspect AnnData"];
  2 -->|output_h5ad| 13;
  f9e862db-eb22-4124-8fe0-0fdcfcfb8393["Output\nGeneral - Filterbygenes"];
  13 --> f9e862db-eb22-4124-8fe0-0fdcfcfb8393;
  style f9e862db-eb22-4124-8fe0-0fdcfcfb8393 stroke:#2c3143,stroke-width:4px;
  14["Inspect AnnData"];
  12 -->|output_h5ad| 14;
  794f72b5-c2c3-46a0-ac15-b9f1e94941d2["Output\nGeneral - Filterbycounts"];
  14 --> 794f72b5-c2c3-46a0-ac15-b9f1e94941d2;
  style 794f72b5-c2c3-46a0-ac15-b9f1e94941d2 stroke:#2c3143,stroke-width:4px;
  15["Scanpy FilterCells"];
  12 -->|output_h5ad| 15;
  b915da66-6435-4871-baa0-3e494ba73c96["Output\nMito-filtered Object"];
  15 --> b915da66-6435-4871-baa0-3e494ba73c96;
  style b915da66-6435-4871-baa0-3e494ba73c96 stroke:#2c3143,stroke-width:4px;
  16["Plot"];
  12 -->|output_h5ad| 16;
  3aef86d7-d34d-4b24-bc97-bf8c97d8d2fa["Output\nViolin - Filterbycounts"];
  16 --> 3aef86d7-d34d-4b24-bc97-bf8c97d8d2fa;
  style 3aef86d7-d34d-4b24-bc97-bf8c97d8d2fa stroke:#2c3143,stroke-width:4px;
  17["Inspect AnnData"];
  15 -->|output_h5ad| 17;
  cd94a4c6-5665-4bdf-88ea-4f4d41efa893["Output\nGeneral - Filterbymito"];
  17 --> cd94a4c6-5665-4bdf-88ea-4f4d41efa893;
  style cd94a4c6-5665-4bdf-88ea-4f4d41efa893 stroke:#2c3143,stroke-width:4px;
  18["Scanpy FilterGenes"];
  15 -->|output_h5ad| 18;
  ee63ef0a-98ed-45cb-b144-1154f84ae452["Output\nFiltered Object"];
  18 --> ee63ef0a-98ed-45cb-b144-1154f84ae452;
  style ee63ef0a-98ed-45cb-b144-1154f84ae452 stroke:#2c3143,stroke-width:4px;
  19["Plot"];
  15 -->|output_h5ad| 19;
  7e48a14f-08fd-45ab-b613-606bf64dcf9d["Output\nViolin - Filterbymito"];
  19 --> 7e48a14f-08fd-45ab-b613-606bf64dcf9d;
  style 7e48a14f-08fd-45ab-b613-606bf64dcf9d stroke:#2c3143,stroke-width:4px;
  20["Inspect AnnData"];
  18 -->|output_h5ad| 20;
  d59efa9b-d049-4f0e-8bd8-8ae982a45d0a["Output\nGeneral - Filtered object"];
  20 --> d59efa9b-d049-4f0e-8bd8-8ae982a45d0a;
  style d59efa9b-d049-4f0e-8bd8-8ae982a45d0a stroke:#2c3143,stroke-width:4px;
  21["Scanpy NormaliseData"];
  18 -->|output_h5ad| 21;
  22["Scanpy FindVariableGenes"];
  21 -->|output_h5ad| 22;
  23["Scanpy ScaleData"];
  22 -->|output_h5ad| 23;
  24["Scanpy RunPCA"];
  23 -->|output_h5ad| 24;
  25["Plot"];
  24 -->|output_h5ad| 25;
  993dea99-990f-460a-beb9-46e5c97ee898["Output\nPCA Variance"];
  25 --> 993dea99-990f-460a-beb9-46e5c97ee898;
  style 993dea99-990f-460a-beb9-46e5c97ee898 stroke:#2c3143,stroke-width:4px;
  26["Scanpy ComputeGraph"];
  24 -->|output_h5ad| 26;
  27["Scanpy RunTSNE"];
  26 -->|output_h5ad| 27;
  28["Scanpy RunUMAP"];
  27 -->|output_h5ad| 28;
  29["Scanpy FindCluster"];
  28 -->|output_h5ad| 29;
  30["Scanpy FindMarkers"];
  29 -->|output_h5ad| 30;
  308b4961-4d50-442b-9bca-bbb1992426ba["Output\nMarkers - cluster"];
  30 --> 308b4961-4d50-442b-9bca-bbb1992426ba;
  style 308b4961-4d50-442b-9bca-bbb1992426ba stroke:#2c3143,stroke-width:4px;
  035bbbce-fb57-48c8-83d5-2b0cd0376541["Output\nFinal object"];
  30 --> 035bbbce-fb57-48c8-83d5-2b0cd0376541;
  style 035bbbce-fb57-48c8-83d5-2b0cd0376541 stroke:#2c3143,stroke-width:4px;
  31["Scanpy FindMarkers"];
  29 -->|output_h5ad| 31;
  1705e219-192a-4f52-9b26-64fcbcd303ea["Output\nMarkers - genotype"];
  31 --> 1705e219-192a-4f52-9b26-64fcbcd303ea;
  style 1705e219-192a-4f52-9b26-64fcbcd303ea stroke:#2c3143,stroke-width:4px;
  32["Scanpy PlotEmbed"];
  30 -->|output_h5ad| 32;
  33["Scanpy PlotEmbed"];
  30 -->|output_h5ad| 33;
  34["Manipulate AnnData"];
  30 -->|output_h5ad| 34;
  35["Scanpy PlotEmbed"];
  30 -->|output_h5ad| 35;
  36["Inspect AnnData"];
  30 -->|output_h5ad| 36;
  37["AnnData Operations"];
  34 -->|anndata| 37;
  30 -->|output_h5ad| 37;
  38["Join two Datasets"];
  30 -->|output_tsv| 38;
  36 -->|var| 38;
  39["Join two Datasets"];
  31 -->|output_tsv| 39;
  36 -->|var| 39;
  40["AnnData Operations"];
  37 -->|output_h5ad| 40;
  a6d48df0-403f-4efc-a75f-9504a960884e["Output\nFinal cell annotated object"];
  40 --> a6d48df0-403f-4efc-a75f-9504a960884e;
  style a6d48df0-403f-4efc-a75f-9504a960884e stroke:#2c3143,stroke-width:4px;
  41["Cut"];
  38 -->|out_file1| 41;
  0ee7f9b6-b065-4e26-93df-6e6e2fe458a9["Output\nMarkers - cluster - named"];
  41 --> 0ee7f9b6-b065-4e26-93df-6e6e2fe458a9;
  style 0ee7f9b6-b065-4e26-93df-6e6e2fe458a9 stroke:#2c3143,stroke-width:4px;
  42["Cut"];
  39 -->|out_file1| 42;
  fdb88faa-9b76-4edb-b89b-427c098a473e["Output\nMarkers - genotype - named"];
  42 --> fdb88faa-9b76-4edb-b89b-427c098a473e;
  style fdb88faa-9b76-4edb-b89b-427c098a473e stroke:#2c3143,stroke-width:4px;
  43["Scanpy PlotEmbed"];
  40 -->|output_h5ad| 43;

Filter, Plot and Explore Single-cell RNA-seq Data updated

Wendi Bacon, Julia Jakiela

Last updated Jun 13, 2023

Launch in Tutorial Mode question
License: CC-BY-4.0
Tests: ✅ Results: Not yet automated

flowchart TD
  0["ℹ️ Input Dataset\nMito-counted AnnData"];
  style 0 stroke:#2c3143,stroke-width:4px;
  1["Inspect AnnData"];
  0 -->|output| 1;
  2["Scanpy FilterCells"];
  0 -->|output| 2;
  6a00b14b-d3f8-4771-963a-76115c8eaf2f["Output\nGenes-filtered Object"];
  2 --> 6a00b14b-d3f8-4771-963a-76115c8eaf2f;
  style 6a00b14b-d3f8-4771-963a-76115c8eaf2f stroke:#2c3143,stroke-width:4px;
  3["Plot"];
  0 -->|output| 3;
  c123842e-b2d2-43ad-81f0-6ba3b45d4021["Output\nScatter - genes x UMIs"];
  3 --> c123842e-b2d2-43ad-81f0-6ba3b45d4021;
  style c123842e-b2d2-43ad-81f0-6ba3b45d4021 stroke:#2c3143,stroke-width:4px;
  4["Plot"];
  0 -->|output| 4;
  f1020d4d-555e-42fa-ba21-52a151b91a5b["Output\nScatter - mito x genes"];
  4 --> f1020d4d-555e-42fa-ba21-52a151b91a5b;
  style f1020d4d-555e-42fa-ba21-52a151b91a5b stroke:#2c3143,stroke-width:4px;
  5["Plot"];
  0 -->|output| 5;
  a7fe6020-8f1e-470a-9ed3-f2b5e95516e0["Output\nViolin - genotype - log"];
  5 --> a7fe6020-8f1e-470a-9ed3-f2b5e95516e0;
  style a7fe6020-8f1e-470a-9ed3-f2b5e95516e0 stroke:#2c3143,stroke-width:4px;
  6["Plot"];
  0 -->|output| 6;
  15e501bc-8ddb-4519-89eb-dde431ea96c1["Output\nViolin - batch - log"];
  6 --> 15e501bc-8ddb-4519-89eb-dde431ea96c1;
  style 15e501bc-8ddb-4519-89eb-dde431ea96c1 stroke:#2c3143,stroke-width:4px;
  7["Inspect AnnData"];
  0 -->|output| 7;
  8["Plot"];
  0 -->|output| 8;
  eff22f46-baa6-4e00-ba82-d5e12ce26ff0["Output\nScatter - mito x UMIs"];
  8 --> eff22f46-baa6-4e00-ba82-d5e12ce26ff0;
  style eff22f46-baa6-4e00-ba82-d5e12ce26ff0 stroke:#2c3143,stroke-width:4px;
  9["Inspect AnnData"];
  0 -->|output| 9;
  10["Plot"];
  0 -->|output| 10;
  56677ca4-129c-476c-85ec-69d1bb3d800d["Output\nViolin - sex - log"];
  10 --> 56677ca4-129c-476c-85ec-69d1bb3d800d;
  style 56677ca4-129c-476c-85ec-69d1bb3d800d stroke:#2c3143,stroke-width:4px;
  11["Plot"];
  2 -->|output_h5ad| 11;
  acb61ea4-bcb9-45db-beef-e2bf1a176701["Output\nViolin - Filterbygenes"];
  11 --> acb61ea4-bcb9-45db-beef-e2bf1a176701;
  style acb61ea4-bcb9-45db-beef-e2bf1a176701 stroke:#2c3143,stroke-width:4px;
  12["Scanpy FilterCells"];
  2 -->|output_h5ad| 12;
  51853662-4519-4229-a2ea-b22a53e7ef73["Output\nCounts-filtered Object"];
  12 --> 51853662-4519-4229-a2ea-b22a53e7ef73;
  style 51853662-4519-4229-a2ea-b22a53e7ef73 stroke:#2c3143,stroke-width:4px;
  13["Inspect AnnData"];
  2 -->|output_h5ad| 13;
  362a7fe6-24bb-4398-ae48-870f4b4bb774["Output\nGeneral - Filterbygenes"];
  13 --> 362a7fe6-24bb-4398-ae48-870f4b4bb774;
  style 362a7fe6-24bb-4398-ae48-870f4b4bb774 stroke:#2c3143,stroke-width:4px;
  14["Inspect AnnData"];
  12 -->|output_h5ad| 14;
  edf24149-9341-4fe7-b10c-3fcf092faaa5["Output\nGeneral - Filterbycounts"];
  14 --> edf24149-9341-4fe7-b10c-3fcf092faaa5;
  style edf24149-9341-4fe7-b10c-3fcf092faaa5 stroke:#2c3143,stroke-width:4px;
  15["Scanpy FilterCells"];
  12 -->|output_h5ad| 15;
  a88ec405-265f-4a59-a75e-34e3b05b0096["Output\nMito-filtered Object"];
  15 --> a88ec405-265f-4a59-a75e-34e3b05b0096;
  style a88ec405-265f-4a59-a75e-34e3b05b0096 stroke:#2c3143,stroke-width:4px;
  16["Plot"];
  12 -->|output_h5ad| 16;
  a7c8b0d9-82d3-4438-a212-b5f7c56d36b8["Output\nViolin - Filterbycounts"];
  16 --> a7c8b0d9-82d3-4438-a212-b5f7c56d36b8;
  style a7c8b0d9-82d3-4438-a212-b5f7c56d36b8 stroke:#2c3143,stroke-width:4px;
  17["Inspect AnnData"];
  15 -->|output_h5ad| 17;
  56882809-e19f-451a-8010-bc55dcee482f["Output\nGeneral - Filterbymito"];
  17 --> 56882809-e19f-451a-8010-bc55dcee482f;
  style 56882809-e19f-451a-8010-bc55dcee482f stroke:#2c3143,stroke-width:4px;
  18["Scanpy FilterGenes"];
  15 -->|output_h5ad| 18;
  00846477-dec5-408a-83b2-105fff7ce05b["Output\nFiltered Object"];
  18 --> 00846477-dec5-408a-83b2-105fff7ce05b;
  style 00846477-dec5-408a-83b2-105fff7ce05b stroke:#2c3143,stroke-width:4px;
  19["Plot"];
  15 -->|output_h5ad| 19;
  7582e113-2004-4255-a1f3-d3123373f342["Output\nViolin - Filterbymito"];
  19 --> 7582e113-2004-4255-a1f3-d3123373f342;
  style 7582e113-2004-4255-a1f3-d3123373f342 stroke:#2c3143,stroke-width:4px;
  20["Inspect AnnData"];
  18 -->|output_h5ad| 20;
  2d870a40-c602-4a1c-afef-450489354d39["Output\nGeneral - Filtered object"];
  20 --> 2d870a40-c602-4a1c-afef-450489354d39;
  style 2d870a40-c602-4a1c-afef-450489354d39 stroke:#2c3143,stroke-width:4px;
  21["Scanpy NormaliseData"];
  18 -->|output_h5ad| 21;
  22["Scanpy FindVariableGenes"];
  21 -->|output_h5ad| 22;
  a0eb92b1-0263-4179-b7af-4bd9bcc9c960["Output\nUse_me_FVG"];
  22 --> a0eb92b1-0263-4179-b7af-4bd9bcc9c960;
  style a0eb92b1-0263-4179-b7af-4bd9bcc9c960 stroke:#2c3143,stroke-width:4px;
  23["Scanpy ScaleData"];
  22 -->|output_h5ad| 23;
  5776dbb9-0cac-40c0-9bae-9accae16a7a0["Output\nUse_me_Scaled"];
  23 --> 5776dbb9-0cac-40c0-9bae-9accae16a7a0;
  style 5776dbb9-0cac-40c0-9bae-9accae16a7a0 stroke:#2c3143,stroke-width:4px;
  24["Scanpy RunPCA"];
  23 -->|output_h5ad| 24;
  25["Plot"];
  24 -->|output_h5ad| 25;
  f0b6f578-050f-4936-9ee7-9956b0760c6f["Output\nPCA Variance"];
  25 --> f0b6f578-050f-4936-9ee7-9956b0760c6f;
  style f0b6f578-050f-4936-9ee7-9956b0760c6f stroke:#2c3143,stroke-width:4px;
  26["Scanpy ComputeGraph"];
  24 -->|output_h5ad| 26;
  27["Scanpy RunTSNE"];
  26 -->|output_h5ad| 27;
  28["Scanpy RunUMAP"];
  27 -->|output_h5ad| 28;
  29["Scanpy FindCluster"];
  28 -->|output_h5ad| 29;
  30["Scanpy FindMarkers"];
  29 -->|output_h5ad| 30;
  7cfe5c9c-1c80-41b4-b669-633b1d7d40e3["Output\nMarkers - cluster"];
  30 --> 7cfe5c9c-1c80-41b4-b669-633b1d7d40e3;
  style 7cfe5c9c-1c80-41b4-b669-633b1d7d40e3 stroke:#2c3143,stroke-width:4px;
  98e98405-951e-4c6c-be01-3c925ae35449["Output\nFinal object"];
  30 --> 98e98405-951e-4c6c-be01-3c925ae35449;
  style 98e98405-951e-4c6c-be01-3c925ae35449 stroke:#2c3143,stroke-width:4px;
  31["Scanpy FindMarkers"];
  29 -->|output_h5ad| 31;
  1e9f229d-eb34-4a5b-a6d9-e70c7b0581f4["Output\nMarkers - genotype"];
  31 --> 1e9f229d-eb34-4a5b-a6d9-e70c7b0581f4;
  style 1e9f229d-eb34-4a5b-a6d9-e70c7b0581f4 stroke:#2c3143,stroke-width:4px;
  32["Scanpy PlotEmbed"];
  30 -->|output_h5ad| 32;
  33["Scanpy PlotEmbed"];
  30 -->|output_h5ad| 33;
  34["Manipulate AnnData"];
  30 -->|output_h5ad| 34;
  35["Scanpy PlotEmbed"];
  30 -->|output_h5ad| 35;
  36["Inspect AnnData"];
  30 -->|output_h5ad| 36;
  37["AnnData Operations"];
  34 -->|anndata| 37;
  30 -->|output_h5ad| 37;
  38["Join two Datasets"];
  30 -->|output_tsv| 38;
  36 -->|var| 38;
  39["Join two Datasets"];
  31 -->|output_tsv| 39;
  36 -->|var| 39;
  40["AnnData Operations"];
  37 -->|output_h5ad| 40;
  10bd70f8-ffcb-442b-9647-e5b947b6d35e["Output\nFinal cell annotated object"];
  40 --> 10bd70f8-ffcb-442b-9647-e5b947b6d35e;
  style 10bd70f8-ffcb-442b-9647-e5b947b6d35e stroke:#2c3143,stroke-width:4px;
  41["Cut"];
  38 -->|out_file1| 41;
  4f822c1c-91c5-4be4-8f9b-d5bdda0a037e["Output\nMarkers - cluster - named"];
  41 --> 4f822c1c-91c5-4be4-8f9b-d5bdda0a037e;
  style 4f822c1c-91c5-4be4-8f9b-d5bdda0a037e stroke:#2c3143,stroke-width:4px;
  42["Cut"];
  39 -->|out_file1| 42;
  3b471f3d-263d-4299-9b7d-8a8ae1aa556e["Output\nMarkers - genotype - named"];
  42 --> 3b471f3d-263d-4299-9b7d-8a8ae1aa556e;
  style 3b471f3d-263d-4299-9b7d-8a8ae1aa556e stroke:#2c3143,stroke-width:4px;
  43["Scanpy PlotEmbed"];
  40 -->|output_h5ad| 43;

Importing into Galaxy

Below are the instructions for importing these workflows directly into your Galaxy server of choice to start using them!

Hands-on: Importing a workflow

Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.

Click on galaxy-upload Import at the top-right of the screen

Provide your workflow

Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”

Option 2: Upload the workflow file in the box labelled “Archived Workflow File”

Click the Import workflow button

Below is a short video demonstrating how to import a workflow from GitHub using this procedure:

Video: Importing a workflow from URL