From acd1e8f5300c1b7c2cb6096434dc13ccbd5f8b20 Mon Sep 17 00:00:00 2001 From: TuomasBorman Date: Mon, 5 Feb 2024 14:24:26 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20EBI-Meta?= =?UTF-8?q?genomics/MGnifyR@64fffce80f8fec3198701d59e0959cc7936a4e73=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/MGnifyR.html | 32 ++++++++++++------ pkgdown.yml | 2 +- .../analyses/MGYA00377505_format_json/.RDS | Bin 1001 -> 1001 bytes .../samples/ERS2967391_format_json/.RDS | Bin 1043 -> 1043 bytes 4 files changed, 23 insertions(+), 11 deletions(-) diff --git a/articles/MGnifyR.html b/articles/MGnifyR.html index faf51076..1a258dc1 100644 --- a/articles/MGnifyR.html +++ b/articles/MGnifyR.html @@ -102,9 +102,8 @@

Introduction

Installation

-

MGnifyR is currently hosted on GitHub, and can be -installed using via devtools. MGnifyR should -be built using the following snippet.

+

MGnifyR is hosted on Bioconductor, and can be installed +using via BiocManager.

 BiocManager::install(MGnifyR)
@@ -230,7 +229,9 @@

Functions for fetching the data

Search data

-

Below, we fetch information on samples of drinking water.

+

doQuery() function can be utilized to search results +such as samples and studies from MGnify database. Below, we fetch +information drinking water samples.

+

The result is a table containing accession IDs and description – in +this case – on samples.

 head(samples)
 #>                  biosample   accession sample-desc
@@ -347,6 +350,8 @@ 
Find relevent analyses
 
 analyses_accessions <- searchAnalysis(mg, "samples", samples$accession)

+
By running the searchAnalysis() function, we get
+analysis IDs of samples that we fed as an input.

 
 head(analyses_accessions)
 #> [1] "MGYA00652201" "MGYA00652185" "MGYA00643487" "MGYA00643486" "MGYA00643485"
@@ -356,9 +361,12 @@ 
Find relevent analysesFetch metadata
 

 
We can now check the metadata to get hint of what kind of data we
-have.

+have. We use getMetadata() function to fetch data based on
+analysis IDs.

 
 analyses_metadata <- getMetadata(mg, analyses_accessions)

+
Metadata includes for example information on how analysis was
+conducted and what kind of samples were analyzed.

 
 head(analyses_metadata)
 #>              analysis_analysis-status analysis_pipeline-version
@@ -684,7 +692,7 @@ 
Fetch microbiome dataTreeSE object is uniquely positioned to support
 SummarizedExperiment-based microbiome data manipulation and
 visualization. Moreover, it enables access to miaverse
-tools. For example, we can estimate diversity of samples.

+tools. For example, we can estimate diversity of samples…

 
 mae[[1]] <- estimateDiversity(mae[[1]], index = "shannon")
 
@@ -694,6 +702,7 @@ 
Fetch microbiome data
 plotColData(mae[[1]], "shannon", x = "sample_environment..biome.")

 

+
… and plot abundances of most abundant phyla.

 
 # Agglomerate data
 altExps(mae[[1]]) <- splitByRanks(mae[[1]])
@@ -705,8 +714,8 @@ 
Fetch microbiome datatop_taxa <- getTopFeatures(altExp(mae[[1]], "Phylum"), 10)
 plotAbundance(altExp(mae[[1]], "Phylum")[top_taxa, ], rank = "Phylum")

 

-
We can perform principal component analysis to microbial profiling
-data by utilizing miaverse tools.

+
We can also perform other analyses such as principal component
+analysis to microbial profiling data by utilizing miaverse tools.

 
 # Apply relative transformation
 mae[[1]] <- transformAssay(mae[[1]], method = "relabundance")
@@ -729,8 +738,10 @@ 
Fetch sequence filessearchFile(), we can search files from the
 database.

 
-# Find list of available downloads, and filter for 
-dl_urls <- searchFile(mg, analyses_accessions, type = "analyses")

+dl_urls <- searchFile(mg, analyses_accessions, type = "analyses")

+
The returned table contains search results related to analyses that
+we fed as an input. The table contains information on file and also URL
+address from where the file can be loaded.

 
 target_urls <- dl_urls[
     dl_urls$attributes.description.label == "Predicted alpha tmRNA", ]
@@ -817,6 +828,7 @@ 
Fetch sequence files# Just select a single file from the target_urls list for demonstration.
 file_url <- target_urls$download_url[[1]]
 cached_location <- getFile(mg, file_url)

+
The function returns a path where the file is stored.

 
 # Where are the files?
 cached_location
diff --git a/pkgdown.yml b/pkgdown.yml
index 1ad39694..1827a07d 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -4,5 +4,5 @@ pkgdown_sha: ~
 articles:
   MGnifyR: MGnifyR.html
   MGnifyR_long: MGnifyR_long.html
-last_built: 2024-02-05T08:45Z
+last_built: 2024-02-05T13:31Z
 
diff --git a/reference/.MGnifyR_cache/analyses/MGYA00377505_format_json/.RDS b/reference/.MGnifyR_cache/analyses/MGYA00377505_format_json/.RDS
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