From d64f9cace7f178747d39118586bb94ae7431e2d6 Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Sat, 5 Oct 2024 08:18:26 +0000 Subject: [PATCH 01/60] add initial tutorial --- .../bulk-deconvolution-evaluate/tutorial.md | 218 ++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md new file mode 100644 index 00000000000000..5c984224c02f7d --- /dev/null +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -0,0 +1,218 @@ +--- +layout: tutorial_hands_on + +title: Evaluating Reference Data for Bulk RNA Deconvolution +subtopic: deconvo +priority: 3 +zenodo_link: '' + +questions: +- How do I evaluate my reference data +- How do I compare different deconvolution tools +- What are the best metrics for determining tool accuracy +objectives: +- Generate psuedo-bulk data from single-cell RNA data. +- Process the single-cell and psuedo-bulk data using various deconvolution tools +- Evaluate and visualse the results of the different deconvolution methods +time_estimation: 2H +key_points: +- Something about deconv tools having different accuracies +- Its important to validate the accuracy of tools + +tags: +- transcriptomics + +contributions: + authorship: + - hexhowells + funding: + - elixir-fair-data + +follow_up_training: + - + type: "internal" + topic_name: single-cell + +--- + + +# Get single-cell data + +> Data upload +> +> 1. Create a new history for this tutorial *"Deconvolution: Evaluating Reference Data"* +> 2. Import the files from [Zenodo]({{ page.zenodo_link }}) or from +> the shared data library (`GTN - Material` -> `{{ page.topic_name }}` +> -> `{{ page.title }}`): +> +> * Human pancreas single-cell RNA datasets (tag: `#scrna`) +> ``` +> https://zenodo.org/record/5719228/files/EMTABesethealthy.expression.tabular +> https://zenodo.org/record/5719228/files/EMTABesethealthy.phenotype.tabular +> ``` +> +> {% snippet faqs/galaxy/datasets_import_via_link.md %} +> +> {% snippet faqs/galaxy/datasets_import_from_data_library.md %} +> +> 3. Rename the datasets +> +> 4. Check the datatype +> +> {% snippet faqs/galaxy/datasets_change_datatype.md datatype="tabular" %} +> +> 5. Add a `#metadata` tag to `EMTABesethealthy.phenotype.tabular` and a `#expression` tag to `EMTABesethealthy.expression.tabular` +> +> {% snippet faqs/galaxy/datasets_add_tag.md %} +> +{: .hands_on} + + + +# Process single-cell data + +## Transpose Expression Matrix + +Inspecting the expression data file, we can see that currently the rows represent genes and columns represent cells. However, this needs to be swapped for the later workflows. So first we will transpose the expression matrix. + +> Transpose expression matrix +> +> 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.8+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Input tabular dataset"*: `EMTABesethealthy.expression.tabular` +> +> 2. **Rename** {% icon galaxy-pencil %} output `Transposed expression matrix` +> +{: .hands_on} + +## Generate Batch Mode Collections + +In order to run our evaluations 20 times we need to provide our workflow with a collection, doing this will run the workflow in "batch mode" which will run the workflow individually for each element in the collection. + +We are going to duplicate our single-cell data 20 times and store it in a collection. This will be done for both the expression data and metadata files. + +> Generate collections from data +> +> 1. {% tool [Duplicate file to collection](__DUPLICATE_FILE_TO_COLLECTION__) %} with the following parameters: +> - {% icon param-file %} *"Input Dataset"*: `EMTABesethealthy.phenotype.tabular` +> - *"Size of output collection"*: `20` +> +> 2. **Rename** {% icon galaxy-pencil %} output `Metadata` +> +> 3. {% tool [Duplicate file to collection](__DUPLICATE_FILE_TO_COLLECTION__) %} with the following parameters: +> - {% icon param-file %} *"Input Dataset"*: `Transposed expression matrix` +> - *"Size of output collection"*: `20` +> +> 4. **Rename** {% icon galaxy-pencil %} output `Expression data` +> +{: .hands_on} + +## Generate Expression Set Objects + +Next we will need to use the single-cell data to build and expression set object, this will be used later in the evaluation when we perform the actual deconvolution. **Note: We are using the original imported data here, not the transposed data or collections.** + +> Build the Expression Set object +> +> 1. {% tool [Construct Expression Set Object](toolshed.g2.bx.psu.edu/repos/bgruening/music_construct_eset/music_construct_eset/0.1.1+galaxy3) %} with the following parameters: +> - {% icon param-file %} *"Assay Data"*: `EMTABesethealthy.expression.tabular` (Input dataset) +> - {% icon param-file %} *"Phenotype Data"*: `EMTABesethealthy.phenotype.tabular` (Input dataset) +> +> > +> > +> > An ExpressionSet object has many data slots, the principle of which are the experiment data (*exprs*), the phenotype data (*pData*), as well metadata pertaining to experiment information and additional annotations (*fData*). +> {: .comment} +> +{: .hands_on} + +Similar to the expression data, this object needs to be duplicated 20 times into a collection for later batch processing. + +> Generate ESet collection +> +> 1. {% tool [Duplicate file to collection](__DUPLICATE_FILE_TO_COLLECTION__) %} with the following parameters: +> - {% icon param-file %} *"Input Dataset"*: `ESet Object` (output of **Construct Expression Set Object** {% icon tool %}) +> - *"Size of output colection"*: `20` +> +> 2. **Rename** {% icon galaxy-pencil %} output `ESet Object` +> +{: .hands_on} + + +# Create pseudo-bulk and actual cell proportions + +Here we are going to run our first workflow, this workflow will extract a subsample from the data containing 200 cells. This data will be used to generate our pseudo-bulk data along with the actual cell proportions used to evaluate/compare with the output of the deconvolutional tools. + +> Inputting Multiple Datasets +> +> In order to upload the input collections into the workflow, you first need to set the input type to **Multiple datasets** in the input file selection. +{: .comment} + +> Run pseudobulk and actual proportions workflow +> +> 1. **Import the workflow** into Galaxy +> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/qc_report.ga) or download it to your computer. +> - Import the workflow into Galaxy +> +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/transcriptomics/tutorials/rna-seq-reads-to-counts/workflows/qc_report.ga" title="QC Report" %} +> +> 2. Run **Workflow pseudobulk and actual proportions** {% icon workflow %} using the following parameters: +> - {% icon param-collection %} *"Metadata"*: `metadata collection` +> - {% icon param-collection %} *"Expression Data"*: `expression data collection` +> +> {% snippet faqs/galaxy/workflows_run.md %} +> 3. Rename output collections / add tags? +> +> 4. Inspect `cell type counts` +{: .hands_on} + +Need to identify the list of cells first before running the second workflow, this can be done by looking at the cell type counts output. For this tutorial we will use all of the cell types as this will be the most realistic scenario. However, cell types with counts below a threshold value (e.g. 10) can be removed. + +# Perform Deconvolution on the Pseudo-Bulk Data + +> Run inferring cellular proportions workflow +> +> 1. **Import the workflow** into Galaxy +> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/qc_report.ga) or download it to your computer. +> - Import the workflow into Galaxy +> +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/transcriptomics/tutorials/rna-seq-reads-to-counts/workflows/qc_report.ga" title="QC Report" %} +> +> 2. Run **Workflow inferring cellular proportions** {% icon workflow %} using the following parameters: +> - {% icon param-collection %} *"Subsample_A - matrix"*: `expression data - A` +> - {% icon param-collection %} *"Subsample_B - matrix"*: `expression data - B` +> - {% icon param-collection %} *"ESet Reference scRNA-seq"*: `ESet Object` +> - *"Cell Types Label from scRNA dataset"*: `cellType` +> - *"Samples Identifier from scRNA dataset"*: `sampleID` +> - *"Cell types to use from scRNA dataset"*:`acinar,alpha,beta,delta,ductal,gamma` +> - {% icon param-collection %} *"B_actuals"*: `actual - B` +> - {% icon param-collection %} *"A_actuals"*: `actual - A` +> +> {% snippet faqs/galaxy/workflows_run.md %} +> 3. Rename output collections / add tags? +> +> 4. Inspect `cell type counts` +{: .hands_on} + +# Compute Accuracy and Error Metrics + +Combine the collection of tables together + +> Combine output tables +> +> 1. {% tool [Column join](toolshed.g2.bx.psu.edu/repos/iuc/collection_column_join/collection_column_join/0.0.3) %} with the following parameters: +> - {% icon param-file %} *"Tabular files"*: `A proportions actual-infer` +> - *"Identifier column"*: `1` +> - *"Number of header lines in each input line"*: `1` +> - *"Fill character"*: `0.0` +> +> 2. **Rename** {% icon galaxy-pencil %} output `Sample A output table` +> +> 3. {% tool [Column join](toolshed.g2.bx.psu.edu/repos/iuc/collection_column_join/collection_column_join/0.0.3) %} with the following parameters: +> - {% icon param-file %} *"Tabular files"*: `B proportions actual-infer` +> - *"Identifier column"*: `1` +> - *"Number of header lines in each input line"*: `1` +> - *"Fill character"*: `0.0` +> +> 4. **Rename** {% icon galaxy-pencil %} output `Sample B output table` +> +{: .hands_on} + +# Visualise outputs From ba0299a8b6f7948d86852c4bc7ad1674b6a9661b Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Sat, 5 Oct 2024 10:12:50 +0000 Subject: [PATCH 02/60] add introduction and agenda --- .../bulk-deconvolution-evaluate/tutorial.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index 5c984224c02f7d..9b5644c40db6b3 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -35,9 +35,23 @@ follow_up_training: --- +There are various methods to estimate the proportions of cell types in bulk RNA data. Since the actual proportions of the data are unknown, how do we know if our tools are producing accurate results? + +In this tutorial we will be using single-cell data with known cell proportions in order to create' pseudo' bulk RNA data. We will then estimate the proportions of this this pseudo-bulk data using the currently available deconvolution tools within Galaxy. Since we know the true proportions values, we will be able to measure and compare the accuracy of the tools. + +> +> +> In this tutorial, we will cover: +> +> 1. TOC +> {:toc} +> +{: .agenda} # Get single-cell data +First we need to create a new history in Galaxy and load our single-cell data We are going to use the single-cell dataset found in a previous deconvolution tutorial [https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music/tutorial.html](https://training.galaxyproject.org/training-material/topics/single-cell/tutorials/bulk-music/tutorial.html). + > Data upload > > 1. Create a new history for this tutorial *"Deconvolution: Evaluating Reference Data"* From 3517cdb1d3f683500a70903bc5f7f9bc67cd2563 Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Tue, 15 Oct 2024 11:50:47 +0000 Subject: [PATCH 03/60] update processing single-cell data section --- .../bulk-deconvolution-evaluate/tutorial.md | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index 9b5644c40db6b3..2c1b6f48910ede 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -85,9 +85,13 @@ First we need to create a new history in Galaxy and load our single-cell data We # Process single-cell data +In order to get a good understanding of the accuracy of our deconvolution tools, we are going to run our evaluations multiple times. This is to ensure that a single good or bad evaluation doesn't become indicative of the overall tool's performance. + +However, instead of running all of our tools multiple times for each evaluation (which would be quite time consuming!), we will leverage "batch computation" in Galaxy. By storing our data in collections, any tools or workflows that use with that data will run individually for each element in the collection. We will now perform some pre-processing of our data to get it into the right format. + ## Transpose Expression Matrix -Inspecting the expression data file, we can see that currently the rows represent genes and columns represent cells. However, this needs to be swapped for the later workflows. So first we will transpose the expression matrix. +If we inspect the expression data file downloaded earlier, we can see that currently the rows represent genes and columns represent cells. However, this needs to be swapped for the later workflows. To fix this we will transpose the expression matrix. > Transpose expression matrix > @@ -100,9 +104,9 @@ Inspecting the expression data file, we can see that currently the rows represen ## Generate Batch Mode Collections -In order to run our evaluations 20 times we need to provide our workflow with a collection, doing this will run the workflow in "batch mode" which will run the workflow individually for each element in the collection. +For this tutorial we will run the evaluations **20** times, this will both help improve the sample size and allow us to determine the consistency of the tools, whilst being small enough to run in a reasonable amount of time! -We are going to duplicate our single-cell data 20 times and store it in a collection. This will be done for both the expression data and metadata files. +We will now duplicate our single-cell data 20 times and store it in a collection. This will be done for both the expression data and metadata files. > Generate collections from data > @@ -122,7 +126,9 @@ We are going to duplicate our single-cell data 20 times and store it in a collec ## Generate Expression Set Objects -Next we will need to use the single-cell data to build and expression set object, this will be used later in the evaluation when we perform the actual deconvolution. **Note: We are using the original imported data here, not the transposed data or collections.** +Next we will need to use the single-cell data to build and expression set object, this will be used later in the evaluation when we perform the actual deconvolution. + +**Note: We are using the original imported data here, not the transposed data or collections.** > Build the Expression Set object > From 4fd1de021469051c2fe78e747fb23ca0d7f6bc22 Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Tue, 15 Oct 2024 12:46:00 +0000 Subject: [PATCH 04/60] update visualise results section --- .../bulk-deconvolution-evaluate/tutorial.md | 121 +++++++++++++++++- 1 file changed, 117 insertions(+), 4 deletions(-) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index 2c1b6f48910ede..c808e2c6b1af99 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -143,7 +143,7 @@ Next we will need to use the single-cell data to build and expression set object > {: .hands_on} -Similar to the expression data, this object needs to be duplicated 20 times into a collection for later batch processing. +Similar to the expression data, this ExpressionSet object needs to be duplicated 20 times into a collection for later batch processing. > Generate ESet collection > @@ -158,7 +158,7 @@ Similar to the expression data, this object needs to be duplicated 20 times into # Create pseudo-bulk and actual cell proportions -Here we are going to run our first workflow, this workflow will extract a subsample from the data containing 200 cells. This data will be used to generate our pseudo-bulk data along with the actual cell proportions used to evaluate/compare with the output of the deconvolutional tools. +We are now going to run our first workflow! This workflow will extract a subsample from the data containing 200 cells. This data will be used to generate our pseudo-bulk data along with the actual cell proportions used to evaluate/compare with the output of the deconvolutional tools. > Inputting Multiple Datasets > @@ -211,9 +211,123 @@ Need to identify the list of cells first before running the second workflow, thi > 4. Inspect `cell type counts` {: .hands_on} +# Visualise Results + +> Transpose output tables +> +> 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.8+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Input tabular dataset"*: `MuSiC x NNLS` +> +> 2. **Rename** {% icon galaxy-pencil %} output `Transposed - MuSiC x NNLS` +> +{: .hands_on} + +Now we need to separate the output tables for MuSiC and NNLS in the transposed collection. + +> Extract MuSiC Table +> +> 1. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.3+galaxy2) %} with the following parameters: +> - {% icon param-file %} *"File to cut"*: `Transposed - MuSiC x NNLS` +> - *"Operation"*: `Discard` +> - *"Cut by"*: `fields` +> - *"Delimited by"*: `Tab` +> - *"Is there a header for the data's columns ?"*: `Yes` +> - *"List of Fields"*: `- c4: NNLS Estimated Proportions of Cell Types +- c5: NNLS Estimated Proportions of Cell Types` +> +> 2. **Rename** {% icon galaxy-pencil %} output `MuSiC Results` +> +{: .hands_on} + +> Extract NNLS Table +> +> 1. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.3+galaxy2) %} with the following parameters: +> - {% icon param-file %} *"File to cut"*: `Transposed - MuSiC x NNLS` +> - *"Operation"*: `Discard` +> - *"Cut by"*: `fields` +> - *"Delimited by"*: `Tab` +> - *"Is there a header for the data's columns ?"*: `Yes` +> - *"List of Fields"*: `- c2: Music Estimated Proportions of Cell Types +- c3: Music Estimated Proportions of Cell Types` +> +> 2. **Rename** {% icon galaxy-pencil %} output `NNLS Results` +> +{: .hands_on} + + +Now instead of having a collection of tables, we need to combine the collections together into a single table for both MuSiC and NNLS. + +> Combine output tables (MuSiC) +> +> 1. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: +> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: `MuSiC Results` +> - *"Keep one header line"*: **Yes** +> +> 2. **Rename** {% icon galaxy-pencil %} output `Combined Music table` +> +> 3. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: +> - *"Remove first"*: `1` +> - {% icon param-file %} *"Tabular files"*: `Combined Music table` +> +> 4. **Rename** {% icon galaxy-pencil %} output `MuSiC Combined` +> +{: .hands_on} + +> Combine output tables (NNLS) +> +> 1. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: +> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: `NNLS Results` +> - *"Keep one header line"*: **Yes** +> +> 2. **Rename** {% icon galaxy-pencil %} output `Combined NNLS table` +> +> 3. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: +> - *"Remove first"*: `1` +> - {% icon param-file %} *"Tabular files"*: `Combined NNLS table` +> +> 4. **Rename** {% icon galaxy-pencil %} output `NNLS Combined` +> +{: .hands_on} + +> Plot the actual and inferred data +> +> 1. {% tool [Scatterplot with ggplot2 +](toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/3.4.0+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Input in tabular format"*: `Combined output` +> - *"Column to plot on x-axis"*: `2` +> - *"Column to plot on y-axis"*: `3` +> - *"Plot title"*: `Correlation between inferred and actual cell-type proportions` +> - *"Label for x axis"*: `Actual proportions` +> - *"Label for y axis"*: `Inferred proportions` +> - In *"Advanced options"*: +> - *"Plotting multiple groups"*: `Plot multiple groups of data on one plot` +> - *"column differentiating the different groups"*: `1` +> - *"Color schemes to differentiate your groups"*: `Paired - predefined color pallete (discrete, max=12 colors)` +> - *"Reverse color scheme"*: `Default order of color scheme` +> - In *"Output options"*: +> - *"width of output"*: `5.0` +> - *"height of output"*: `3.0` +> +> 2. **Rename** {% icon galaxy-pencil %} output `MuSiC Scatterplot` +> +{: .hands_on} + + # Compute Accuracy and Error Metrics -Combine the collection of tables together +Then plot with scatterplot tool + +Then show how it should be more quantitative and can be plotted in different ways + +Workflow -> compute error, compute RMSE, compute Pearson coefficient + +Use error table, filter for certain cell types, plot violin plot + +show the RSME and Pearson coefficient values as quantitative metrics for both error (as seen in the violin plot) and correlation (as shown in the scatter plot) + +so plots first, nice and visual, easy to interpret, then more concrete quantitative results + +need to do this for both MuSiC and NNLS > Combine output tables > @@ -235,4 +349,3 @@ Combine the collection of tables together > {: .hands_on} -# Visualise outputs From d48d424134e0fffb40f7252f590919dfc37afdc4 Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Tue, 15 Oct 2024 12:56:40 +0000 Subject: [PATCH 05/60] update lformatting for the lst of fields parameterrs --- .../bulk-deconvolution-evaluate/tutorial.md | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index c808e2c6b1af99..da947308d8d800 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -232,8 +232,11 @@ Now we need to separate the output tables for MuSiC and NNLS in the transposed c > - *"Cut by"*: `fields` > - *"Delimited by"*: `Tab` > - *"Is there a header for the data's columns ?"*: `Yes` -> - *"List of Fields"*: `- c4: NNLS Estimated Proportions of Cell Types -- c5: NNLS Estimated Proportions of Cell Types` +> - *"List of Fields"*: +``` +c4: NNLS Estimated Proportions of Cell Types +c5: NNLS Estimated Proportions of Cell Types +``` > > 2. **Rename** {% icon galaxy-pencil %} output `MuSiC Results` > @@ -247,8 +250,11 @@ Now we need to separate the output tables for MuSiC and NNLS in the transposed c > - *"Cut by"*: `fields` > - *"Delimited by"*: `Tab` > - *"Is there a header for the data's columns ?"*: `Yes` -> - *"List of Fields"*: `- c2: Music Estimated Proportions of Cell Types -- c3: Music Estimated Proportions of Cell Types` +> - *"List of Fields"*: +``` +c2: Music Estimated Proportions of Cell Types +c3: Music Estimated Proportions of Cell Types +``` > > 2. **Rename** {% icon galaxy-pencil %} output `NNLS Results` > From 6d7aacc67f370d11d0f8938d57f419fdbe3d3f0c Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Tue, 15 Oct 2024 13:00:48 +0000 Subject: [PATCH 06/60] remove newline in scatterplot tool metadata --- .../tutorials/bulk-deconvolution-evaluate/tutorial.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index da947308d8d800..e798d8351b477e 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -297,8 +297,7 @@ Now instead of having a collection of tables, we need to combine the collections > Plot the actual and inferred data > -> 1. {% tool [Scatterplot with ggplot2 -](toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/3.4.0+galaxy1) %} with the following parameters: +> 1. {% tool [Scatterplot with ggplot2](toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/3.4.0+galaxy1) %} with the following parameters: > - {% icon param-file %} *"Input in tabular format"*: `Combined output` > - *"Column to plot on x-axis"*: `2` > - *"Column to plot on y-axis"*: `3` From 655ac6b7afdd877e19657722280e52ad5e9c68dc Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Tue, 15 Oct 2024 13:08:12 +0000 Subject: [PATCH 07/60] add scatterplot tool nstrution forrNNLS --- .../bulk-deconvolution-evaluate/tutorial.md | 24 ++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index e798d8351b477e..ea12f39825542c 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -213,6 +213,8 @@ Need to identify the list of cells first before running the second workflow, thi # Visualise Results +## Pre-process the output results + > Transpose output tables > > 1. {% tool [Transpose](toolshed.g2.bx.psu.edu/repos/iuc/datamash_transpose/datamash_transpose/1.8+galaxy1) %} with the following parameters: @@ -295,10 +297,12 @@ Now instead of having a collection of tables, we need to combine the collections > {: .hands_on} +## Plot scatter plots of the results + > Plot the actual and inferred data > > 1. {% tool [Scatterplot with ggplot2](toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/3.4.0+galaxy1) %} with the following parameters: -> - {% icon param-file %} *"Input in tabular format"*: `Combined output` +> - {% icon param-file %} *"Input in tabular format"*: `MuSiC Combined` > - *"Column to plot on x-axis"*: `2` > - *"Column to plot on y-axis"*: `3` > - *"Plot title"*: `Correlation between inferred and actual cell-type proportions` @@ -315,6 +319,24 @@ Now instead of having a collection of tables, we need to combine the collections > > 2. **Rename** {% icon galaxy-pencil %} output `MuSiC Scatterplot` > +> 3. {% tool [Scatterplot with ggplot2](toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/3.4.0+galaxy1) %} with the following parameters: +> - {% icon param-file %} *"Input in tabular format"*: `NNLS Combined` +> - *"Column to plot on x-axis"*: `2` +> - *"Column to plot on y-axis"*: `3` +> - *"Plot title"*: `Correlation between inferred and actual cell-type proportions` +> - *"Label for x axis"*: `Actual proportions` +> - *"Label for y axis"*: `Inferred proportions` +> - In *"Advanced options"*: +> - *"Plotting multiple groups"*: `Plot multiple groups of data on one plot` +> - *"column differentiating the different groups"*: `1` +> - *"Color schemes to differentiate your groups"*: `Paired - predefined color pallete (discrete, max=12 colors)` +> - *"Reverse color scheme"*: `Default order of color scheme` +> - In *"Output options"*: +> - *"width of output"*: `5.0` +> - *"height of output"*: `3.0` +> +> 4. **Rename** {% icon galaxy-pencil %} output `NNLS Scatterplot` +> {: .hands_on} From 68c1216669c4e3ad168fa456b6bd787e7b9705eb Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Tue, 15 Oct 2024 15:13:43 +0000 Subject: [PATCH 08/60] re-order remove header tool before combine collections --- .../bulk-deconvolution-evaluate/tutorial.md | 32 ++++++++----------- 1 file changed, 14 insertions(+), 18 deletions(-) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index ea12f39825542c..1d6db32d4fc3b0 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -267,33 +267,29 @@ Now instead of having a collection of tables, we need to combine the collections > Combine output tables (MuSiC) > -> 1. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: -> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: `MuSiC Results` -> - *"Keep one header line"*: **Yes** -> -> 2. **Rename** {% icon galaxy-pencil %} output `Combined Music table` +> 1. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: +> - *"Remove first"*: `2` +> - {% icon param-file %} *"Tabular files"*: `MuSiC Results` > -> 3. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: -> - *"Remove first"*: `1` -> - {% icon param-file %} *"Tabular files"*: `Combined Music table` +> 2. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: +> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: (output of **Remove beginning** {% icon tool %}) +> - *"Keep one header line"*: **Yes** > -> 4. **Rename** {% icon galaxy-pencil %} output `MuSiC Combined` +> 3. **Rename** {% icon galaxy-pencil %} output `MuSiC Combined` > {: .hands_on} > Combine output tables (NNLS) > -> 1. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: -> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: `NNLS Results` -> - *"Keep one header line"*: **Yes** -> -> 2. **Rename** {% icon galaxy-pencil %} output `Combined NNLS table` +> 1. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: +> - *"Remove first"*: `2` +> - {% icon param-file %} *"Tabular files"*: `NNLS Results` > -> 3. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: -> - *"Remove first"*: `1` -> - {% icon param-file %} *"Tabular files"*: `Combined NNLS table` +> 2. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: +> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: (output of **Remove beginning** {% icon tool %}) +> - *"Keep one header line"*: **Yes** > -> 4. **Rename** {% icon galaxy-pencil %} output `NNLS Combined` +> 3. **Rename** {% icon galaxy-pencil %} output `NNLS Combined` > {: .hands_on} From b9eea601eaf82673557ea85d1f02f99483bd7b93 Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Mon, 4 Nov 2024 20:43:13 +0000 Subject: [PATCH 09/60] update visualisation and metrics section --- .../bulk-deconvolution-evaluate/tutorial.md | 118 +++++++++++------- 1 file changed, 74 insertions(+), 44 deletions(-) diff --git a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md index 1d6db32d4fc3b0..b2709695f585fd 100644 --- a/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md +++ b/topics/single-cell/tutorials/bulk-deconvolution-evaluate/tutorial.md @@ -263,34 +263,21 @@ c3: Music Estimated Proportions of Cell Types {: .hands_on} -Now instead of having a collection of tables, we need to combine the collections together into a single table for both MuSiC and NNLS. - -> Combine output tables (MuSiC) -> -> 1. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: -> - *"Remove first"*: `2` -> - {% icon param-file %} *"Tabular files"*: `MuSiC Results` -> -> 2. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: -> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: (output of **Remove beginning** {% icon tool %}) -> - *"Keep one header line"*: **Yes** +> Run visualisation pre-processing workflow > -> 3. **Rename** {% icon galaxy-pencil %} output `MuSiC Combined` -> -{: .hands_on} - -> Combine output tables (NNLS) +> 1. **Import the workflow** into Galaxy +> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/qc_report.ga) or download it to your computer. +> - Import the workflow into Galaxy > -> 1. {% tool [Remove beginning](Remove beginning1) %} with the following parameters: -> - *"Remove first"*: `2` -> - {% icon param-file %} *"Tabular files"*: `NNLS Results` +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/transcriptomics/tutorials/rna-seq-reads-to-counts/workflows/qc_report.ga" title="QC Report" %} > -> 2. {% tool [Collapse Collection](toolshed.g2.bx.psu.edu/repos/nml/collapse_collections/collapse_dataset/5.1.0) %} with the following parameters: -> - {% icon param-file %} *"Collection of files to collapse into single dataset"*: (output of **Remove beginning** {% icon tool %}) -> - *"Keep one header line"*: **Yes** +> 2. Run **Workflow preprocess visualisations** {% icon workflow %} using the following parameters: +> - {% icon param-collection %} *"Cell Proportions"*: `Music Results` > -> 3. **Rename** {% icon galaxy-pencil %} output `NNLS Combined` +> 3. Run **Workflow preprocess visualisations** {% icon workflow %} using the following parameters: +> - {% icon param-collection %} *"Cell Proportions"*: `NNLS Results` > +> {% snippet faqs/galaxy/workflows_run.md %} {: .hands_on} ## Plot scatter plots of the results @@ -336,39 +323,82 @@ Now instead of having a collection of tables, we need to combine the collections {: .hands_on} -# Compute Accuracy and Error Metrics +## Plot Violin plots of the error + +Next we will plot the distribution of errors between the predicted and actual cellular proportions for a select number of cell types. We could plot all cell types in the output, however too many will cause the visualisations to be messy and difficult to interpret. + +If we inspect the cell counts table from earlier, we will see the following (note the actual counts will differ from this tutorial since the subsamples are random): + +- table here - + +We can see that most cell types have very low proportions, so for this visualisation we will only look at 5 cell types with the highest proportion values. For the above table these cell types are: `alpha, beta, gamma, ductal, acinar`. Before we visualise the data we first need to extract only these cell types from the error table. + +> Extract Cell Types +> +> 1. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.3+galaxy2) %} with the following parameters: +> - {% icon param-file %} *"File to cut"*: `Error Table (Music)` +> - *"Operation"*: `Discard` +> - *"Cut by"*: `fields` +> - *"Delimited by"*: `Tab` +> - *"Is there a header for the data's columns ?"*: `Yes` +> - *"List of Fields"*: `Select the columns containing: alpha, beta, gamma, ductal, acinar` +> +> 2. **Rename** {% icon galaxy-pencil %} output `Music Errors` +> +> 3. {% tool [Advanced Cut](toolshed.g2.bx.psu.edu/repos/bgruening/text_processing/tp_cut_tool/9.3+galaxy2) %} with the following parameters: +> - {% icon param-file %} *"File to cut"*: `Error Table (NNLS)` +> - *"Operation"*: `Discard` +> - *"Cut by"*: `fields` +> - *"Delimited by"*: `Tab` +> - *"Is there a header for the data's columns ?"*: `Yes` +> - *"List of Fields"*: `Select the columns containing: alpha, beta, gamma, ductal, acinar` +> +> 4. **Rename** {% icon galaxy-pencil %} output `NNLS Errors` +> +{: .hands_on} + + +# Compute Accuracy Metrics -Then plot with scatterplot tool +Visualisations are a great tool for getting an intuitive overview of the data. However, some of the interpretations from visualisations can be subjective. Having quantitative results alongside visualisations can offer concrete and precise values about the data that can more easily be compared. We will use two different quantitative metrics in this tutorial; Pearson correlation and RMSE. -Then show how it should be more quantitative and can be plotted in different ways +## Pearson Correlation -Workflow -> compute error, compute RMSE, compute Pearson coefficient +The Pearson correlation coefficient is a statistical value that represents the direction and correlation between two variables, the value of this metric ranges between -1 and 1, where: -Use error table, filter for certain cell types, plot violin plot +- -1 = negative correlation +- 0 = no correlation +- 1 = positive correlation -show the RSME and Pearson coefficient values as quantitative metrics for both error (as seen in the violin plot) and correlation (as shown in the scatter plot) +The equation for calculating the Pearson correlation can be seen below, the workflow to compute this metric breaks down this formula into smaller steps. -so plots first, nice and visual, easy to interpret, then more concrete quantitative results +- insert formula - -need to do this for both MuSiC and NNLS -> Combine output tables +## Root Mean Squared Error (RMSE) + +Root Mean Squared Error or RMSE is a + +- insert formula - + + +## Compute Metrics + +> Run visualisation workflow > -> 1. {% tool [Column join](toolshed.g2.bx.psu.edu/repos/iuc/collection_column_join/collection_column_join/0.0.3) %} with the following parameters: -> - {% icon param-file %} *"Tabular files"*: `A proportions actual-infer` -> - *"Identifier column"*: `1` -> - *"Number of header lines in each input line"*: `1` -> - *"Fill character"*: `0.0` +> 1. **Import the workflow** into Galaxy +> - Copy the URL (e.g. via right-click) of [this workflow]({{ site.baseurl }}{{ page.dir }}workflows/qc_report.ga) or download it to your computer. +> - Import the workflow into Galaxy > -> 2. **Rename** {% icon galaxy-pencil %} output `Sample A output table` +> {% snippet faqs/galaxy/workflows_run_trs.md path="topics/transcriptomics/tutorials/rna-seq-reads-to-counts/workflows/qc_report.ga" title="QC Report" %} > -> 3. {% tool [Column join](toolshed.g2.bx.psu.edu/repos/iuc/collection_column_join/collection_column_join/0.0.3) %} with the following parameters: -> - {% icon param-file %} *"Tabular files"*: `B proportions actual-infer` -> - *"Identifier column"*: `1` -> - *"Number of header lines in each input line"*: `1` -> - *"Fill character"*: `0.0` +> 2. Run **Workflow preprocess visualisations** {% icon workflow %} using the following parameters: +> - {% icon param-collection %} *"Cell Proportions"*: `Music Results` > -> 4. **Rename** {% icon galaxy-pencil %} output `Sample B output table` +> 3. Run **Workflow preprocess visualisations** {% icon workflow %} using the following parameters: +> - {% icon param-collection %} *"Cell Proportions"*: `NNLS Results` > +> {% snippet faqs/galaxy/workflows_run.md %} {: .hands_on} + From 51cbbec1e0293c6ca410d806126d48c9f8255345 Mon Sep 17 00:00:00 2001 From: Morgan Howells Date: Fri, 8 Nov 2024 12:43:19 +0000 Subject: [PATCH 10/60] add equation diagrams --- .../bulk-deconvolution-evaluate/pearson.png | Bin 0 -> 204222 bytes .../bulk-deconvolution-evaluate/rmse.png | Bin 0 -> 114523 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 topics/single-cell/images/bulk-deconvolution-evaluate/pearson.png create mode 100644 topics/single-cell/images/bulk-deconvolution-evaluate/rmse.png diff --git a/topics/single-cell/images/bulk-deconvolution-evaluate/pearson.png b/topics/single-cell/images/bulk-deconvolution-evaluate/pearson.png new file mode 100644 index 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