seandavi

{
  "accession": "GSE000123",
  "files": [
  {
    "filetype": "Series SOFT file",
    "name": "GSE227465_family.soft.gz",
    "size": 23413,
    "md5sum": "....",
    "created_at": "DATE",
    "updated_at": "DATE"

seandavi

---
format:
  html:
    mermaid-format: svg
---

```{mermaid}
%%| fig-width: 100%
    autonumber
    Participant C as Client[<font size=6>]

seandavi

The ENA browser API https://www.ebi.ac.uk/ena/portal/api/swagger-ui/

There is only limit, no offset. API is designed to simply stream large resultsets

Search by SRA study ID

Output as TSV

SEARCH_QUERY='secondary_study_accession=SRP082656' && curl "https://www.ebi.ac.uk/ena/portal/api/search?query=${SEARCH_QUERY}&result=read_run&fields=experiment_accession%2Cexperiment_title%2Csecondary_study_accession%2Caligned%2Caltitude%2Cassembly_quality%2Cassembly_software%2Cbam_aspera%2Cbam_bytes%2Cbam_ftp%2Cbam_galaxy%2Cbam_md5%2Cbase_count%2Cbinning_software%2Cbio_material%2Cbisulfite_protocol%2Cbroad_scale_environmental_context%2Cbroker_name%2Ccage_protocol%2Ccell_line%2Ccell_type%2Ccenter_name%2Cchecklist%2Cchip_ab_provider%2Cchip_protocol%2Cchip_target%2Ccollected_by%2Ccollection_date%2Ccollection_date_end%2Ccollection_date_start%2Ccompleteness_score%2Ccontamination_score%2Ccontrol_experiment%2Ccountry%2Ccultivar%2Cculture_collection%2Cdatahub%2Cdepth%2Cdescription%2Cdev_stage%2Cdnase_protocol%2Cecotype%2

seandavi

datasets:
  - name: brfss
    title: The brfss dataset
    description: |
      a very long description which can be 
      in [markdown](https://markdown.org).
      - list item
      - list item 2
    processor: readr::read_csv
  - name: svi

seandavi

"""This script scrapes the state cancer profiles website
and saves the results as csv files.

The website is a bit of a mess, so this script is a bit of a mess.
It requires scraping the select options from the website
and then iterating over all the possible combinations of
select options to get the data.

The script is designed to be run from the command line
with the following command:

seandavi

See: https://cloud.google.com/vertex-ai/docs/generative-ai/chat/test-chat-prompts

You'll need:

1. A project id
2. An installed and working gcloud command-line tool
3. A service account that has been granted access to vertex AI
4. A json key file associated with the service account
5. Set up local environment to use the json key for authentication

Tasks 1, 3, and 4 can be performed using the console OR using gcloud by a user authorized to create resources. Note that the

seandavi

from python:3.11

RUN pip install --upgrade pip
RUN pip install omicidx
RUN pip install fsspec gcsfs s3fs

COPY abc.py .

seandavi

library(httr2)
ChatGPT <- function(prompt='Tell me about Bioconductor') {
  base_request = httr2::request('https://openai-playground-asdf.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2023-03-15-preview')
  res = base_request |>
    httr2::req_headers('api-key'=Sys.getenv('API_KEY')) |>
    httr2::req_body_json(list(
        messages=
          list(
            list(role="system",content="You are a helpful AI assistant"),
            list(role="user", content=prompt)

seandavi

digraph ChatGPT {
    node [shape=box]
    subgraph cluster_0 {
        label="Microsoft Asure"
        style=dashed
        ChatGPT 
    }
    subgraph cluster_1 {
        label="On Prem or Cloud"
        style="dashed"

seandavi

library(alabaster)
library(AnnotationHub)
library(jsonlite)
ah = AnnotationHub()

STAGE_DIR = '/tmp/ah_staging'
dir.create(STAGE_DIR, showWarnings = FALSE)

write_error = function(dirname, e) {
  jsonlite::write_json(list(message=jsonlite::unbox(e$message)), file.path(dirname,'error.json'))