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simplifies mgnify_query notebook for faster rendering
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@SandyRogers
SandyRogers committed Mar 10, 2023
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Showing 1 changed file with 65 additions and 108 deletions.
173 changes: 65 additions & 108 deletions src/notebooks/R Examples/Search for Samples or Studies.ipynb
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"cell_type": "raw",
"id": "c77864f8-222e-4907-94f7-dc6711e2f7a6",
"metadata": {
"jupyter": {
"source_hidden": true
},
"tags": []
},
"source": [
"---\n",
"title: \"Search for Samples or Studies\"\n",
"author: \"Sandy R (MGnify team)\"\n",
"author: \"Sandy R (MGnify team) and Ben Allen\"\n",
"categories: [R]\n",
"execute: \n",
" enabled: true\n",
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"cell_type": "code",
"execution_count": null,
"id": "6cfcb483-889c-4d26-b2e2-c325f8a66283",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"library(IRdisplay)\n",
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"cell_type": "code",
"execution_count": null,
"id": "bb99acc5-6bfe-4d93-b258-6885505df8db",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"library(vegan)\n",
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"tags": []
},
"source": [
"## Example: find Polar samples "
"## Example: find Polar samples \n",
"In these examples we set `maxhits=1` to retrieve only the first page of results. You can change the limit or set it to `-1` to retrieve all samples matching the query."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83e3d3f1-701b-4528-a6a6-44ac8637f385",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"samps_np <- mgnify_query(mg, \"samples\", latitude_gte=88, maxhits=-1)\n",
"samps_sp <- mgnify_query(mg, \"samples\", latitude_lte=-88, maxhits=-1)\n",
"samps_np <- mgnify_query(mg, \"samples\", latitude_gte=88, maxhits=1)\n",
"samps_sp <- mgnify_query(mg, \"samples\", latitude_lte=-88, maxhits=1)\n",
"samps_polar <- rbind(samps_np, samps_sp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bbeaf601-0b08-4954-a169-41c2a5971f18",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"head(samps_polar)"
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"cell_type": "code",
"execution_count": null,
"id": "1587a68f-d855-4bb8-a540-829aebc80f44",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"studies_ww <- mgnify_query(mg, \"studies\", biome_name=\"wastewater\", maxhits=-1)"
"studies_ww <- mgnify_query(mg, \"studies\", biome_name=\"wastewater\", maxhits=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d825d7a4-319c-419a-b5c0-9b2bab3dd87a",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"head(studies_ww)"
Expand All @@ -179,116 +195,53 @@
"id": "f6edc0c5-48a8-4b8d-a227-b82991007935",
"metadata": {},
"source": [
"## More Sample filters"
]
},
{
"cell_type": "markdown",
"id": "e975a310-262a-4870-b208-35b8c7868133",
"metadata": {},
"source": [
"### By location"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cea4da31-788d-4f1d-8f22-fa5bf131dd07",
"metadata": {},
"outputs": [],
"source": [
"more_northerly_than <- mgnify_query(mg, \"samples\", latitude_gte=88, maxhits=-1)\n",
"## More filters to try:\n",
"\n",
"more_southerly_than <- mgnify_query(mg, \"samples\", latitude_lte=-88, maxhits=-1)\n",
"### Samples by location\n",
"\n",
"more_easterly_than <- mgnify_query(mg, \"samples\", longitude_gte=170, maxhits=-1)\n",
"```R\n",
"more_northerly_than <- mgnify_query(mg, \"samples\", latitude_gte=88, maxhits=1)\n",
"\n",
"more_westerly_than <- mgnify_query(mg, \"samples\", longitude_lte=170, maxhits=-1)\n",
"more_southerly_than <- mgnify_query(mg, \"samples\", latitude_lte=-88, maxhits=1)\n",
"\n",
"at_location <- mgnify_query(mg, \"samples\", geo_loc_name=\"usa\", maxhits=-1)"
]
},
{
"cell_type": "markdown",
"id": "612eae01-4049-4f8e-90bc-866aedffd908",
"metadata": {},
"source": [
"### By biome"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97075a7b-3159-42c0-96c4-6a517370b1c4",
"metadata": {},
"outputs": [],
"source": [
"biome_within_wastewater <- mgnify_query(mg, \"samples\", biome_name=\"wastewater\", maxhits=-1)"
]
},
{
"cell_type": "markdown",
"id": "533541ce-b64e-43a5-984b-0e13970bc2cb",
"metadata": {},
"source": [
"### By metadata\n",
"more_easterly_than <- mgnify_query(mg, \"samples\", longitude_gte=170, maxhits=1)\n",
"\n",
"more_westerly_than <- mgnify_query(mg, \"samples\", longitude_lte=170, maxhits=1)\n",
"\n",
"at_location <- mgnify_query(mg, \"samples\", geo_loc_name=\"usa\", maxhits=1)\n",
"```\n",
"\n",
"### Samples by biome\n",
"```R\n",
"biome_within_wastewater <- mgnify_query(mg, \"samples\", biome_name=\"wastewater\", maxhits=1)\n",
"```\n",
"\n",
"### Samples by metadata\n",
"There are a large number of metadata key:value pairs, because these are author-submitted, along with the samples, to the ENA archive.\n",
"\n",
"If you know how to specify the metadata key:value query for the samples you're interested in, you can use this form to find matching Samples:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b2a26cb-98ce-483c-88d1-c6b0fe149030",
"metadata": {},
"outputs": [],
"source": [
"from_ex_smokers <- mgnify_query(mg, \"samples\", metadata_key=\"smoker\", metadata_value=\"ex-smoker\", maxhits=-1)"
]
},
{
"cell_type": "markdown",
"id": "c554cea5-5ccd-44ed-bfef-a790eea6bfed",
"metadata": {},
"source": [
"If you know how to specify the metadata key:value query for the samples you're interested in, you can use this form to find matching Samples:\n",
"\n",
"```R\n",
"from_ex_smokers <- mgnify_query(mg, \"samples\", metadata_key=\"smoker\", metadata_value=\"ex-smoker\", maxhits=-1)\n",
"```\n",
"To find `metadata_key`s and values, it is best to browse the [interactive API Browser](https://www.ebi.ac.uk/metagenomics/v1/samples), and use the `Filters` button to construct queries interactively at first.\n",
"\n",
"### Studies by centre name\n",
"```R\n",
"from_smithsonian <- mgnify_query(mg, \"studies\", centre_name=\"Smithsonian\", maxhits=-1)\n",
"```\n",
"\n",
"To find `metadata_key`s and values, it is best to browse the [interactive API Browser](https://www.ebi.ac.uk/metagenomics/v1/samples), and use the `Filters` button to construct queries interactively at first."
]
},
{
"cell_type": "markdown",
"id": "6eb4e359-5905-4722-ab88-65a9b3e6e9c2",
"metadata": {},
"source": [
"--- \n",
"## More Study filters"
]
},
{
"cell_type": "markdown",
"id": "0d3ea380-2f3f-4fdd-97c3-eef9d24a3800",
"metadata": {},
"source": [
"### By Centre Name"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c670e399-7297-44f3-951b-58040a9ac0d4",
"metadata": {},
"outputs": [],
"source": [
"from_smithsonian <- mgnify_query(mg, \"studies\", centre_name=\"Smithsonian\", maxhits=-1)"
]
},
{
"cell_type": "markdown",
"id": "fe1f615b-68ec-4b60-bd87-74d3f6fe016b",
"metadata": {},
"source": [
"---\n",
"\n",
"\n",
"## Example: adding additional filters to the data frame"
]
},
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"cell_type": "code",
"execution_count": null,
"id": "1dd0f6c8-0430-4ad2-adef-fb7c5c776fa5",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"lentic_samples <- mgnify_query(mg, \"samples\", biome_name=\"root:Environmental:Aquatic:Lentic\", usecache=T)"
Expand All @@ -315,14 +270,16 @@
"id": "7d5c00be-be94-4368-9141-2ec66e8e16ce",
"metadata": {},
"source": [
"Not, also filter by depth *within* the returned results, using normal R syntax."
"Now, also filter by depth *within* the returned results, using normal R syntax."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e0a11f8-bb7c-4cd6-a314-9cb3ed7b4e0f",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"depth_numeric = as.numeric(lentic_samples$depth) # We must convert data from MGnifyR (always strings) to numerical format.\n",
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