Merge pull request #70 from avantikalal/release-updates

minor updates to notebook text
NVIDIA-Genomics-Research · Mar 25, 2021 · d330d52 · d330d52
2 parents f4e285d + 74b5188
commit d330d52
Show file tree

Hide file tree

Showing 5 changed files with 10 additions and 10 deletions.
diff --git a/notebooks/1M_brain_gpu_analysis_uvm.ipynb b/notebooks/1M_brain_gpu_analysis_uvm.ipynb
@@ -31,7 +31,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For demonstration purposes, we use a dataset of 1M brain cells with Unified Virtual Memory to oversubscribe GPU memory. See the README for instructions to download this dataset."
+    "For demonstration purposes, we use a dataset of 1M brain cells with Unified Virtual Memory to oversubscribe GPU memory."
    ]
   },
   {

diff --git a/notebooks/5k_pbmc_coverage_gpu.ipynb b/notebooks/5k_pbmc_coverage_gpu.ipynb
@@ -27,7 +27,7 @@
     "\n",
     "In this demonstration, we first cluster the PBMC cells similarly to previous examples. Next, we use cuDF to calculate and visualize chromatin accessibility in selected marker regions for multiple clusters. Finally, we use AtacWorks, a deep learning model to improve the accuracy of the chromatin accessibility track and call peaks in individual clusters. We show how AtacWorks can be used to characterize rare populations of cells and identify cell-type specific peaks.\n",
     "\n",
-    "The code for AtacWorks is available [here](https://github.com/clara-parabricks/AtacWorks) and the method is described in detail in [this preprint](https://www.biorxiv.org/content/10.1101/829481v2). AtacWorks is available to run only on NVIDIA GPUs."
+    "The code for AtacWorks is available [here](https://github.com/clara-parabricks/AtacWorks) and the method is described in detail in [this paper](https://www.nature.com/articles/s41467-021-21765-5). AtacWorks is available to run only on NVIDIA GPUs."
    ]
   },
   {
@@ -1335,7 +1335,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/dsci_bmmc_60k_gpu.ipynb b/notebooks/dsci_bmmc_60k_gpu.ipynb
@@ -793,7 +793,7 @@
    "source": [
     "## Find Differential peaks\n",
     "\n",
-    "We perform an accelerated logistic regression-based differential peak computation using RAPIDS. The current release of RAPIDS (as of 4 Aug 2020) has an issue causing the output to differ from that of scikit-learn logistic regression, so this function may not give the exact same results as the equivalent Scanpy CPU function."
+    "We perform an accelerated logistic regression-based differential peak computation using RAPIDS. This function may not give the exact same results as the equivalent Scanpy CPU function."
    ]
   },
   {
@@ -923,7 +923,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

diff --git a/notebooks/hlca_lung_gpu_analysis-visualization.ipynb b/notebooks/hlca_lung_gpu_analysis-visualization.ipynb
@@ -31,7 +31,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For demonstration purposes, we use a dataset of ~70,000 human lung cells from Travaglini et al. 2020 (https://www.biorxiv.org/content/10.1101/742320v2) and label cells using the ACE2, TMPRSS2, and EPCAM marker genes. See the README for instructions to download this dataset."
+    "For demonstration purposes, we use a dataset of ~70,000 human lung cells from Travaglini et al. 2020 (https://www.biorxiv.org/content/10.1101/742320v2) and label cells using the ACE2, TMPRSS2, and EPCAM marker genes."
    ]
   },
   {
@@ -954,4 +954,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
diff --git a/notebooks/hlca_lung_gpu_analysis.ipynb b/notebooks/hlca_lung_gpu_analysis.ipynb
@@ -31,7 +31,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For demonstration purposes, we use a dataset of ~70,000 human lung cells from Travaglini et al. 2020 (https://www.biorxiv.org/content/10.1101/742320v2) and label cells using the ACE2 and TMPRSS2 genes. See the README for instructions to download this dataset."
+    "For demonstration purposes, we use a dataset of ~70,000 human lung cells from Travaglini et al. 2020 (https://www.biorxiv.org/content/10.1101/742320v2) and label cells using the ACE2 and TMPRSS2 genes."
    ]
   },
   {
@@ -1830,7 +1830,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python (rapidgenomics)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1844,7 +1844,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.8"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,