Merge pull request #51 from rajewsky-lab/docs_update_ms

danilexn · web-flow · commit 6ecb3e40988b · 2024-07-11T13:43:52.000+02:00
docs: replace preprint by paper
diff --git a/README.md b/README.md
@@ -12,7 +12,7 @@
 
 # Open-ST: open-source spatial transcriptomics
 
-### [🌐 website](https://rajewsky-lab.github.io/openst/latest) | [📜 preprint](https://www.biorxiv.org/content/10.1101/2023.12.22.572554v1) | [🐁 datasets](https://rajewsky-lab.github.io/openst/latest/examples/datasets/)
+### [🌐 website](https://rajewsky-lab.github.io/openst/latest) | [📜 paper](https://authors.elsevier.com/c/1jJckL7PXqR3U) | [🐁 datasets](https://rajewsky-lab.github.io/openst/latest/examples/datasets/)
 
 Open-ST is an open-source spatial transcriptomics method 
 with efficient whole-transcriptome capture at sub-cellular resolution (0.6 μm) at low cost 
diff --git a/docs/api.md b/docs/api.md
@@ -30,7 +30,7 @@ options:
 
 ## `image_stitch`
 Stitch image fields of view into a single image. Currently, it only supports `--microscope keyence`, for the
-default microscopy setup used in our preprint.
+default microscopy setup used in our paper.
 
 Usage:
 ```text
diff --git a/docs/examples/adult_mouse/introduction.md b/docs/examples/adult_mouse/introduction.md
@@ -1,5 +1,5 @@
 # Introduction
 
-In our preprint, we used an adult mouse hippocampus sample to carefully benchmark the precision, sensitivity, and spatial resolution of RNA capture due to the high availability of published gene expression data (RNA-seq, in-situ hybridization, ST, etc.) and the possibility to maintain RNA quality by controlling sample handling and timing. 
+In our paper, we used an adult mouse hippocampus sample to carefully benchmark the precision, sensitivity, and spatial resolution of RNA capture due to the high availability of published gene expression data (RNA-seq, in-situ hybridization, ST, etc.) and the possibility to maintain RNA quality by controlling sample handling and timing. 
 
 In the following sections, we explore how to reproduce the preprocessing steps for this data, and provide an example notebook for exploratory data analysis of the data using standard single cell tools.
diff --git a/docs/examples/datasets.md b/docs/examples/datasets.md
@@ -1,10 +1,15 @@
 # Datasets
 
-Here we provide a single table with all the links, size and md5 checksums for the publicly available files provided in the following tutorials.
+Here we provide a single table with some links, size and md5 checksums for the files provided in the following tutorials.
+
+
+!!! tip
+
+    All raw reads for the datasets showcased in our paper are available at [SRA](https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA1055947), and all processed objects (H&E images and spatial transcriptomes) are available at [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE251926)
 
 !!! tip
 
-    When using Linux or macOS, we recommend using [`wget`](https://www.gnu.org/software/wget/manual/wget.html) to download these data
+    When downloading any of the files below, using Linux or macOS, we recommend [`wget`](https://www.gnu.org/software/wget/manual/wget.html).
 
 ## Adult mouse hippocampus
 | File | Link | md5 checksum |
diff --git a/docs/examples/e13_mouse/introduction.md b/docs/examples/e13_mouse/introduction.md
@@ -1,5 +1,5 @@
 # Introduction
 
-In our preprint, we used an E13 mouse head sample to benchmark the precision, sensitivity, and spatial resolution of RNA capture due to the high availability of published gene expression data (RNA-seq, in-situ hybridization, ST, etc.) and the possibility to maintain RNA quality by controlling sample handling and timing. 
+In our paper, we used an E13 mouse head sample to benchmark the precision, sensitivity, and spatial resolution of RNA capture due to the high availability of published gene expression data (RNA-seq, in-situ hybridization, ST, etc.) and the possibility to maintain RNA quality by controlling sample handling and timing. 
 
 In the following sections, we explore how to reproduce the preprocessing steps for this data, and provide an example notebook for exploratory data analysis of the data using standard single cell tools.
diff --git a/docs/examples/getting_started.md b/docs/examples/getting_started.md
@@ -3,23 +3,19 @@
 Here you will find datasets generated in the [Rajewsky lab @ MDC Berlin](https://www.mdc-berlin.de/n-rajewsky)
 leveraging the **Open-ST** workflow (experimental and computational). 
 
-_We will add all (clean) notebooks from the preprint soon!_
-
 ## Datasets
 
 We provide raw data, instructions on how to reproduce the preprocessing pipeline, the corresponding preprocessed
-data (for comparison), and notebooks for some exploratory visualization and downstream analysis. We publicly provide mouse datasets, showcased in the [preprint](https://www.biorxiv.org/content/10.1101/2023.12.22.572554v1).
+data (for comparison), and notebooks for some exploratory visualization and downstream analysis. We publicly provide mouse datasets, showcased in the [paper](https://authors.elsevier.com/c/1jJckL7PXqR3U).
 
 <div class="grid cards" markdown>
 
 - :fontawesome-solid-brain: __[Adult mouse hippocampus]__
 - :fontawesome-solid-brain: __[E13 mouse head]__
+- :fontawesome-solid-person: __[Human HNSCC, healthy and metastatic lymph node]__
 
 </div>
 
   [Adult mouse hippocampus]: adult_mouse/introduction.md
   [E13 mouse head]: e13_mouse/introduction.md
-
-
-!!! Note
-    Human data, showcased in the [preprint](https://www.biorxiv.org/content/10.1101/2023.12.22.572554v1), will be publicly released with the paper.
+  [Human HNSCC, healthy and metastatic lymph node]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE251926
diff --git a/docs/experimental/capture_area_generation.md b/docs/experimental/capture_area_generation.md
@@ -10,7 +10,7 @@ For instance, you can get ~360 capture areas sized 3x4 mm from a single **Illumi
 
 When using an **Illumina® NovaSeq 6000 S4** flow cell (35 cycles), sequence the HDMI32-DraI library
 (see in [Oligonucleotides](getting_started.md)) at a loading concentration of 200 pM. 
-Using 200 pM library, loaded according to the KAPA qPCR value, we obtained the following quality metrics for the barcoded fc_1 used in our [our preprint](https://www.biorxiv.org/content/10.1101/2023.12.22.572554v1.article-info): Q30 >= 86%; PF = 78%; occupied = 97%. Although great results were achieved using this flow cell, 97% occupied is high. Consequently, we suggest to use a titration of library loading concentrations (one concentration per lane) when generating your first barcoded flow cell. 
+Using 200 pM library, loaded according to the KAPA qPCR value, we obtained the following quality metrics for the barcoded fc_1 used in our [our paper](https://authors.elsevier.com/c/1jJckL7PXqR3U): Q30 >= 86%; PF = 78%; occupied = 97%. Although great results were achieved using this flow cell, 97% occupied is high. Consequently, we suggest to use a titration of library loading concentrations (one concentration per lane) when generating your first barcoded flow cell. 
 
 Sequence a single-end 37 cycle read, using Read1-DraI oligo as a custom primer.
 Use a custom sequencing [recipe](../static/metadata_files/NovaSeq6000_S4_barcoding_seq_recipe.xml) that stops the run immediately after read 1 prior to on-instrument washes. 
diff --git a/docs/introduction.md b/docs/introduction.md
@@ -1,6 +1,6 @@
 Welcome! Here, we provide comprehensive resources to help you generate and analyse Open-ST data in your lab.
  
-Open-ST is a spatial transcriptomics method that enables efficient whole-transcriptome capture at subcellular resolution. In [our preprint](https://www.biorxiv.org/content/10.1101/2023.12.22.572554v1.article-info), we have demonstrated Open-ST's wide applicability, showing robust transcriptome capture across various mouse and human tissues. 
+Open-ST is a spatial transcriptomics method that enables efficient whole-transcriptome capture at subcellular resolution. In [our paper](https://authors.elsevier.com/c/1jJckL7PXqR3U), we have demonstrated Open-ST's wide applicability, showing robust transcriptome capture across various mouse and human tissues. 
 Our method is cost-efficient, straightforward to employ, and includes open-source software for seamless data processing and analysis.
 
 Here, you can find detailed step-by-step descriptions of the experimental and the computational workflows. In a FAQ section we address commonly asked questions. Via our [discussion board] and our [chat], you can submit your own questions, and we will do our best to provide answers. 
diff --git a/docs/theme_override_home/home.html b/docs/theme_override_home/home.html
@@ -238,8 +238,8 @@ <h1><img src="static/logos/openst_logo_transparent_completeO.png" width="60px" s
           <a href="{{ page.next_page.url | url }}" title="{{ page.next_page.title | striptags }}" class="md-button md-button--primary">
             Get started
           </a>
-          <a href="https://www.biorxiv.org/content/10.1101/2023.12.22.572554v1" title="Preprint" class="md-button">
-            Preprint
+          <a href="https://authors.elsevier.com/c/1jJckL7PXqR3U" title="Paper" class="md-button">
+            Paper
           </a><br>
           <a href="{{ config.repo_url }}" title="{{ lang.t('source.link.title') }}" class="md-button">
             GitHub
