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⬆️ Update package and benchmark pipeline
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‎.Rprofile

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library(tools)
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source(file_path_as_absolute("renv/activate.R"))
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# options(renv.config.pak.enabled=TRUE)
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lib_path <- file_path_as_absolute("conda/lib")
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Sys.setenv(LD_LIBRARY_PATH = paste(lib_path, Sys.getenv("LD_LIBRARY_PATH"), sep = ":"))
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library(reticulate)
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use_python(paste0(getwd(),"/conda/bin/python"))
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print(.libPaths())
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print(py_config())
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print(Sys.getenv("LD_LIBRARY_PATH"))
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import("scanpy")
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import("anndata")
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options(bitmapType = "cairo")
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‎.gitignore

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*.pth
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*.zip
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*.csv
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*.txt
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*.rds
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*.pickle
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*.pkl
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*.RData
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*.Rhistory
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*.pdf
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*.png
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*.gz
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*.whl
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*.html
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# folder
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**/conda/

‎benchmark/.gitignore

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multi/
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results_bak/
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figures/
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hetero/
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hetero/
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perturb/

‎benchmark/analysis/2d_vis_align.ipynb

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‎benchmark/analysis/aggr_datasets.ipynb

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‎benchmark/analysis/benchmark_analysis.ipynb

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‎benchmark/analysis/celltype_region_analysis.ipynb

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‎benchmark/analysis/neighbor_celltype.ipynb

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"text": [
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"--------Visium---------\n",
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"contain 3635 cells\n",
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"{True: 2901, False: 734}\n",
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"0.7980742778541953\n",
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"{True: 2896, False: 739}\n",
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"0.7966987620357634\n",
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"\n",
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"\n",
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"contain 3566 cells\n",
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"{True: 2693, False: 873}\n",
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"0.7551878855860908\n",
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"{True: 2684, False: 882}\n",
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"0.7526640493550196\n",
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"\n",
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"\n",
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"--------MERFISH---------\n",
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"contain 6412 cells\n",
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"{False: 4251, True: 2161}\n",
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"0.337024329382408\n",
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"{False: 4252, True: 2160}\n",
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"0.3368683718028696\n",
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"\n",
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"\n",
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"contain 6507 cells\n",
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"\n",
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"--------Stereo-seq---------\n",
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"contain 101837 cells\n",
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"{True: 91285, False: 10552}\n",
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"0.8963834362756169\n",
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"{True: 91247, False: 10590}\n",
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"0.8960102909551538\n",
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"\n",
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"\n",
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"contain 96221 cells\n",
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"{True: 85333, False: 10888}\n",
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"0.886843828270336\n",
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"{True: 85279, False: 10942}\n",
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"0.8862826202180397\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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" <th>0</th>\n",
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" <td>slice_0</td>\n",
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" <td>Visium</td>\n",
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" <td>0.798074</td>\n",
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" <td>0.796699</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>slice_1</td>\n",
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" <td>Visium</td>\n",
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" <td>0.755188</td>\n",
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" <td>0.752664</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>slice_0</td>\n",
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" <td>MERFISH</td>\n",
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" <td>0.337024</td>\n",
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" <td>0.336868</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
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" <td>slice_0</td>\n",
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" <td>Stereo-seq</td>\n",
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" <td>0.896383</td>\n",
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" <td>0.896010</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>slice_1</td>\n",
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" <td>Stereo-seq</td>\n",
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" <td>0.886844</td>\n",
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" <td>0.886283</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" index variable value\n",
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"0 slice_0 Visium 0.798074\n",
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"1 slice_1 Visium 0.755188\n",
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"2 slice_0 MERFISH 0.337024\n",
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"0 slice_0 Visium 0.796699\n",
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"1 slice_1 Visium 0.752664\n",
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"2 slice_0 MERFISH 0.336868\n",
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"3 slice_1 MERFISH 0.371907\n",
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"4 slice_0 Stereo-seq 0.896383\n",
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"5 slice_1 Stereo-seq 0.886844"
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"4 slice_0 Stereo-seq 0.896010\n",
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"5 slice_1 Stereo-seq 0.886283"
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]
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},
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"execution_count": 29,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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"result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"result.to_csv('neighbor_celltype.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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"version": "3.8.17"
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},
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"vscode": {
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"interpreter": {

‎benchmark/analysis/plot_slices.ipynb

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‎benchmark/analysis/scalability_analysis.ipynb

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 1,
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"id": "a48d3846-e540-4cbd-8ac0-ae8efcccc645",
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"metadata": {
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"tags": [],
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"vscode": {
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"languageId": "r"
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}
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},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1] \"/lustre/grp/gglab/xiacr/SLAT/renv/library/R-4.1/x86_64-pc-linux-gnu\" \n",
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"[2] \"/lustre/grp/gglab/xiacr/SLAT/renv/sandbox/R-4.1/x86_64-pc-linux-gnu/90a76c8d\"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Warning message in system2(\"poetry\", c(\"env\", \"info\", \"--path\"), stdout = TRUE):\n",
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"“running command ''poetry' env info --path' had status 1”\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"python: /lustre/grp/gglab/xiacr/SLAT/conda/bin/python\n",
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"libpython: /lustre/grp/gglab/xiacr/SLAT/conda/lib/libpython3.8.so\n",
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"pythonhome: /lustre/grp/gglab/xiacr/SLAT/conda:/lustre/grp/gglab/xiacr/SLAT/conda\n",
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"version: 3.8.17 | packaged by conda-forge | (default, Jun 16 2023, 07:06:00) [GCC 11.4.0]\n",
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"numpy: /lustre/grp/gglab/xiacr/SLAT/conda/lib/python3.8/site-packages/numpy\n",
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"numpy_version: 1.24.4\n",
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"\n",
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"NOTE: Python version was forced by use_python function\n",
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"[1] \"/lustre/grp/gglab/xiacr/SLAT/conda/lib:/lustre/grp/gglab/xiacr/SLAT/resource/R/lib/R/lib:/usr/local/lib:/flash/grp/gglab/xiacr/SpatialLR-dev/resource/jdk1.8.0_361/jre/lib/i386/client:/opt/slurm-22.05.2/lib:/opt/slurm-22.05.2/lib/slurm:\"\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.2 ──\n",
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"\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.8 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.0.10\n",
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"\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.2.1 \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.5.0 \n",
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"\u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.3 \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.2 \n",
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"\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.0 \n",
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"── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
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"\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
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"\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
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"\n",
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"Attaching package: ‘reshape2’\n",
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"\n",
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"\n",
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"The following object is masked from ‘package:tidyr’:\n",
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"\n",
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" smiths\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"setwd('../../')\n",
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"source('.Rprofile')\n",

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