| digraph ChatGPT { | |
| node [shape=box] | |
| subgraph cluster_0 { | |
| label="Microsoft Asure" | |
| style=dashed | |
| ChatGPT | |
| } | |
| subgraph cluster_1 { | |
| label="On Prem or Cloud" | |
| style="dashed" |
| 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')) |
You are an AI assistant that summarizes meeting notes generated by Zoom. For the meeting notes provided as input, provide these items if possible from the content:
| # Place the cursor inside "graph" to get some refactoring options | |
| digraph G { | |
| fontname="Helvetica,Arial,sans-serif" | |
| node [fontname="Helvetica,Arial,sans-serif"] | |
| edge [fontname="Helvetica,Arial,sans-serif"] | |
| edge[color="#00000050"] | |
| subgraph cluster_0 { |
| I would like you to describe the UCSC genome browser table called "cytoBand". The first few lines of the table are here: | |
| chrom chromStart chromEnd name gieStain | |
| chr1 0 2300000 p36.33 gneg | |
| chr1 2300000 5300000 p36.32 gpos25 | |
| chr1 5300000 7100000 p36.31 gneg | |
| chr1 7100000 9100000 p36.23 gpos25 | |
| chr1 9100000 12500000 p36.22 gneg | |
| chr1 12500000 15900000 p36.21 gpos50 | |
| chr1 15900000 20100000 p36.13 gneg |
| # <[email protected]>, 2023-06-02 | |
| # | |
| # Terraform for setting up GPT-4, GPT-3.5-turbo, and text-embedding-ada-002 | |
| # endpoints. Note that not all models are available in all regions, so | |
| # check before changing the region here, currently set to "southcentralus" | |
| # | |
| # Assumes az-cli authenticated (requires Azure subscription) and terraform | |
| # available and installed | |
| # | |
| terraform { |
| --- | |
| title: "data-engineering-R" | |
| --- | |
| ## Background^[From https://www.stitchdata.com/columnardatabase/] | |
| Suppose you're a retailer maintaining a web-based storefront. An ecommerce site generates a lot of data. Consider product purchase transactions: | |
|  |
| {"title":"Mesothelioma_52413","status":"Public on Dec 23 2022","submission_date":"2022-08-05","last_update_date":"2022-12-23","type":"genomic","anchor":null,"contact":{"city":"Nagoya","name":{"first":"Shinya","middle":"","last":"Toyokuni"},"email":"[email protected]","state":"Aichi","address":"65 Tsuruma-Cho, Showa-Ku","department":"Pathology","country":"Japan","web_link":null,"institute":"Nagoya University","zip_postal_code":null,"phone":null},"description":null,"accession":"GSM6433302","biosample":null,"tag_count":null,"tag_length":null,"platform_id":"GPL10451","hyb_protocol":"The labeled DNA was hybridized with Agilent SurePrint G3 Mouse CGH 4x180k microarray at 67°C for 24 hours according to the manufacturer's protocol (Version 8.0).","channel_count":2,"scan_protocol":"The slides were scanned in an Agilent DNA microarray scanner with SureScan High-Resolution Technology (G2565CA).","data_row_count":174012,"library_source":null,"overall_design":null,"sra_experiment":null,"data_processing":"The sc |
| ## ----------------------------------------------------------------------------- | |
| ## GEOquery | |
| ## ----------------------------------------------------------------------------- | |
| library(GEOquery) | |
| gse = getGEO("GSE103512")[[1]] | |
| ## ----------------------------------------------------------------------------- | |
| library(SummarizedExperiment) | |
| se = as(gse, "SummarizedExperiment") |
| ## ----message=FALSE,warning=FALSE---------------------------------------------- | |
| pkgs = c( | |
| "ggplot2", | |
| "GEOquery", | |
| "SummarizedExperiment" | |
| ) | |
| ins = installed.packages(repos = BiocManager::repositories()) | |
| for(pkg in pkgs) { | |
| if(!(pkg %in% rownames(ins))) | |
| BiocManager::install(pkg) |