remove unnecessary skips

Author: scottgigante-immunai

CONTRIBUTING.md

@@ -56,21 +56,21 @@ below:
    documentation helpful.
    ![](<https://i.imgur.com/fUcpLYl.png>" width=400px>
 4. Set up [tower.nf](https://tower.nf) and make sure you have access to
-   [`openproblems-bio`](https://tower.nf/orgs/openproblems-bio/workspaces/openproblems-bio/watch). <!-- markdownlint-disable-line MD013 -->
+   [`openproblems-bio`](https://tower.nf/orgs/openproblems-bio/workspaces/openproblems-bio/watch).
    If you do not have access, please contact us at
-   [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com). <!-- markdownlint-disable-line MD013 -->
+   [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com).
 5. Create repository secrets (*not environment secrets*)
-   [https://github.com/USERNAME/openproblems/settings/secrets](https://github.com/USERNAME/openproblems/settings/secrets)  <!-- markdownlint-disable-line MD013 -->
+   [https://github.com/USERNAME/openproblems/settings/secrets](https://github.com/USERNAME/openproblems/settings/secrets)
     * *AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are included in your AWS login
       details. If you do not have these, please contact us at
-      [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com).* <!-- markdownlint-disable-line MD013 -->
+      [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com).*
     * *TOWER_ACCESS_KEY: log in with GitHub to <https://tower.nf> and create a token at
       <https://tower.nf/tokens>.*
     * When you are done with this step, your page should look like this:
     ![AWS secrets success](static/img/AWS_secret.png)
 
 6. Enable workflows at
-   [https://github.com/USERNAME/openproblems/actions](https://github.com/USERNAME/openproblems/actions). <!-- markdownlint-disable-line MD013 -->
+   [https://github.com/USERNAME/openproblems/actions](https://github.com/USERNAME/openproblems/actions).
 7. Set up your git repository to fetch branches from `base` at
    `openproblems-bio/openproblems`
 
@@ -93,7 +93,7 @@ below:
     ```
 
    To generate an SSH key and add it to your GitHub account, follow [this tutorial from
-   GitHub](https://docs.github.com/en/github/authenticating-to-github/adding-a-new-ssh-key-to-your-github-account). <!-- markdownlint-disable-line MD013 -->
+   GitHub](https://docs.github.com/en/github/authenticating-to-github/adding-a-new-ssh-key-to-your-github-account).
 
 8. Create a new branch for your task (**no underscores or spaces allowed**). It is best
    to coordinate with other people working on the same feature as you so that there
@@ -118,7 +118,7 @@ below:
 9. Sometimes, changes might be made to the openproblems `base` repository that you want
    to incorporate into your fork. To sync your fork from `base`, use the following code
    adapted from the [Syncing a
-   Fork](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork) <!-- markdownlint-disable-line MD013 -->
+   Fork](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork)
    tutorial from GitHub.
 
     ```shell
@@ -169,7 +169,7 @@ If your method takes hyperparameters, set them as keyword arguments in the metho
 definition. If the hyperparameters change depending on the value of `test`, set the
 keyword argument to `None` and set them to your chosen defaults only if the passed value
 is `None`. For an example, see [harmonic
-alignment](openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py). <!-- markdownlint-disable-line MD013 -->
+alignment](openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py).
 
 Metrics should take an AnnData object and return a `float`.
 
@@ -268,7 +268,7 @@ function](./openproblems/tools/normalize.py) (e.g.,
 `adata.layers["log_scran_pooling"]`).
 
 To see a gold standard loader, look at
-[openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py](./openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py) <!-- markdownlint-disable-line MD013 -->
+[openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py](./openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py)
 
 This file name should match
 `[First Author Last Name]_[Year Published]_short_Description_of_data.py`. E.g. the
@@ -287,7 +287,7 @@ from .f2 import f2
 ```
 
 to
-[`openproblems/tasks/label_projection/metrics/__init__.py`](openproblems/tasks/label_projection/metrics/__init__.py). <!-- markdownlint-disable-line MD013 -->
+[`openproblems/tasks/label_projection/metrics/__init__.py`](openproblems/tasks/label_projection/metrics/__init__.py).
 
 For datasets in particular, these should be loaded using a `loader` function from
 `openproblems.data`, with only task-specific annotations added in the task-specific data

README.md

@@ -17,7 +17,7 @@ Formalizing and benchmarking open problems in single-cell genomics.
 * For contributing guidelines, see [CONTRIBUTING.md](CONTRIBUTING.md)  
 * For instructions on editing Docker images, see [docker/README.md](docker/README.md)  
 * For instructions on using the `openproblems-cli`, see
-  [openproblems/api/README.md](https://github.com/singlecellopenproblems/SingleCellOpenProblems/tree/master/openproblems/api) <!-- markdownlint-disable-line MD013 -->
+  [openproblems/api/README.md](https://github.com/singlecellopenproblems/SingleCellOpenProblems/tree/master/openproblems/api)
 * For a walkthrough of the GitHub Actions workflows and AWS Sagemaker, see
   [SAGEMAKER.md](SAGEMAKER.md)  
 * For a description of existing an proposed tasks, see [TASKS.md](TASKS.md)  

TASKS.md

@@ -36,7 +36,7 @@ consistency between the gene scores and gene expression across cells.
 
 Two representative software
 [ArchR](https://www.archrproject.com/bookdown/calculating-gene-scores-in-archr.html) and
-[MAESTRO](https://github.com/liulab-dfci/MAESTRO/blob/d58fc186a4329febde3b0d5b213c2d0edf8de44c/example/Gene_activity_modelling/Gene_activity_modelling.md), <!-- markdownlint-disable-line MD013 -->
+[MAESTRO](https://github.com/liulab-dfci/MAESTRO/blob/d58fc186a4329febde3b0d5b213c2d0edf8de44c/example/Gene_activity_modelling/Gene_activity_modelling.md),
 each has different way of assigning peak weights upon genes include a set of methods,
 but also share some common property. A typical algorithm includes the following common
 steps:
@@ -112,7 +112,7 @@ experiments have been growing in terms of the number of cells measured. Original
 cutting-edge SmartSeq experiments would yield a few hundred cells, at best. Now, it is
 not uncommon to see experiments that yield over [100,000
 cells](https://www.nature.com/articles/s41586-018-0590-4) or even [>1 million
-cells.](https://www.10xgenomics.com/blog/our-13-million-single-cell-dataset-is-ready-to-download) <!-- markdownlint-disable-line MD013 -->
+cells.](https://www.10xgenomics.com/blog/our-13-million-single-cell-dataset-is-ready-to-download)
 
 Each *feature* in a dataset functions as a single dimension. While each of the ~30,000
 dimensions measured in each cell (not accounting for roughly 75-90% data dropout per

docker/README.md

@@ -142,15 +142,15 @@ cd workflow && snakemake -j 10 docker_build
 Docker images are built by the `run_benchmarks` GitHub Actions workflow on both the base
 repository and on forks. As long as you have AWS secrets configured properly for your
 repository (see our [Contributing
-Guide](https://github.com/openproblems-bio/openproblems/blob/main/CONTRIBUTING.md#submitting-new-features)), <!-- markdownlint-disable-line MD013 -->
+Guide](https://github.com/openproblems-bio/openproblems/blob/main/CONTRIBUTING.md#submitting-new-features)),
 these images will be uploaded to Amazon Web Services [Elastic Container
 Registry](https://aws.amazon.com/ecr/) (ECR). You can then download the image locally or
 attach to AWS SageMaker Studio.
 
 Once your Run Benchmark has completed successfully, you should see a pane in the GitHub
 Actions tab of your fork that looks like this:
 
-![Successful Actions run](https://user-images.githubusercontent.com/8322751/112719533-c508e100-8ecf-11eb-91b0-6f99ccee2e3f.png)  <!-- markdownlint-disable-line MD013 -->
+![Successful Actions run](https://user-images.githubusercontent.com/8322751/112719533-c508e100-8ecf-11eb-91b0-6f99ccee2e3f.png)
 
 If that workflow failed, you should look at the workflow logs to find the error.
 
@@ -165,12 +165,12 @@ As you can see below, images uploaded to the ECR have Image Tags in the followin
 `openproblems-python37-scgen` image. This is converted to an Image Tag
 `daniel-batch-integration-openproblems-python37-scgen`.
 
-![ECR example screen](https://user-images.githubusercontent.com/8322751/112719414-43b14e80-8ecf-11eb-8fe2-5588e42c77c5.png)  <!-- markdownlint-disable-line MD013 -->
+![ECR example screen](https://user-images.githubusercontent.com/8322751/112719414-43b14e80-8ecf-11eb-8fe2-5588e42c77c5.png)
 
 ## Pulling images from the ECR to your local machine
 
 To pull images from the ECR using `docker pull`, first download and setup the
-[`amazon-ecr-credential-helper`](https://github.com/awslabs/amazon-ecr-credential-helper)  <!-- markdownlint-disable-line MD013 -->
+[`amazon-ecr-credential-helper`](https://github.com/awslabs/amazon-ecr-credential-helper)
 using the same AWS secrets that you used to set up your fork repository. With that set
 up you can use the following command to pull the image:
 

openproblems/tasks/dimensionality_reduction/README.md

@@ -10,7 +10,7 @@ scRNA-seq experiments have been growing in terms of the number of cells measured
 Originally, cutting-edge SmartSeq experiments would yield a few hundred cells, at best.
 Now, it is not uncommon to see experiments that yield over [100,000 cells]
 (<https://www.nature.com/articles/s41586-018-0590-4>) or even [> 1 million
-cells.](https://www.10xgenomics.com/blog/our-13-million-single-cell-dataset-is-ready-to-download) <!-- markdownlint-disable-line MD013 -->
+cells.](https://www.10xgenomics.com/blog/our-13-million-single-cell-dataset-is-ready-to-download)
 
 Each *feature* in a dataset functions as a single dimension. While each of the ~30,000
 dimensions measured in each cell (not accounting for roughly 75-90% data dropout per
@@ -29,7 +29,7 @@ the data for visualization and interpretation.
 [Reference](https://www.tandfonline.com/doi/abs/10.1080/14786440109462720)
 
 *Adapted from the [sklearn
-documentation](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)*.  <!-- markdownlint-disable-line MD013 -->
+documentation](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)*.
 
 Linear dimensionality reduction using Singular Value Decomposition of the data to
 project it to a lower dimensional space. The input data is centered but not scaled for
@@ -43,7 +43,7 @@ by [scanpy](https://scanpy.readthedocs.io/en/stable/generated/scanpy.tl.pca.html
 [Reference](https://arxiv.org/abs/1802.03426)
 
 *Adapted from the [sklearn
-documentation](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)*.  <!-- markdownlint-disable-line MD013 -->
+documentation](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html)*.
 
 t-SNE is a tool to visualize high-dimensional data. It converts similarities between
 data points to joint probabilities and tries to minimize the Kullback-Leibler divergence
@@ -159,7 +159,7 @@ of the ***topography*** of the data in high-dimensional space.
 ### Trustworthiness
 
 *Adapted from the [sklearn
-documentation.](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html)* <!-- markdownlint-disable-line MD013 -->
+documentation.](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html)*
 
 Trustworthiness expresses to what extent the local structure in an embedding is
 retained. The trustworthiness is within [0, 1]. It is defined as

openproblems/tasks/label_projection/README.md

@@ -31,7 +31,7 @@ assigns cell type labels to cells in the test set.
 
 * **Accuracy**: Average number of correctly applied labels.
 * **F1 score**: The [F1
-  score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) <!-- markdownlint-disable-line MD013 -->
+  score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html)
   is a weighted average of the precision and recall over all class labels, where an F1
   score reaches its best value at 1 and worst score at 0, where each class contributes
   to the score relative to its frequency in the dataset.