There are 8 data upload methods:
Both 16s and metagenomics generated data are supported here. The three required files are:
Make sure:
Example files:
Instructions:
Note: Each uploaded table will show up in the right panel.
Running time: < 1s
There are three example datasets available in animalcules: a synthetic toy dataset (already loaded), a TB dataset, and an asthma dataset. Users could use any dataset to try all functions and features in animalcules.
Instructions:
Running time: < 0.5s
In animalcules, users could choose to save their dataset to a .rds file in the Tab 2 (Summary and Filter). Later, users could load this saved dataset by uploading the .rds file to animalcules easily via this animalcules file upload option.
Running time: < 0.5s
Phyloseq object can be easily read into animalcules with the following steps. Suppose the phyloseq object in R is called ‘physeq’. First, write count table, taxonomy table, and sample data table to csv files:
library(phyloseq)
write.csv(otu_table(physeq), "count.csv")
write.csv(tax_table(physeq), "tax.csv")
write.csv(sample_data(physeq), "sample.csv")
Then, follow the same ‘count file’ upload steps as shown above with the three csv files.
To generate a phyloseq object from the animalcules MAE object is also very simple:
library("phyloseq")
# Extract data
microbe <- MAE[["MicrobeGenetics"]]
# host <- MAE[['HostGenetics']]
tax_table <- as.matrix(as.data.frame(rowData(microbe))) # organism x taxlev
sam_table <- as.data.frame(colData(microbe)) # sample x condition
counts_table <- as.matrix(as.data.frame(assays(microbe))[, rownames(sam_table)]) # organism x sample
# build phyloseq objecy
OTU <- otu_table(counts_table, taxa_are_rows = TRUE)
TAX <- tax_table(tax_table)
SAM <- sample_data(sam_table)
physeq <- phyloseq(OTU, TAX, SAM)
Select the “Alternative Upload” option to upload additional file types, including: * BIOM file * Count file with tax id * PathoScope file * animalcules-id file
Make sure:
Instructions:
Running time: < 0.5s
Both 16s and metagenomics generated data are supported here. The two required files are:
Make sure:
Instructions:
Note: Each uploaded table will show up in the right panel.
Running time: < 1s
To analyze pathoscope outputs, users need to upload pathoscope reports (use browser for multiple reports upload), as well as an annotation file containing metadata for each sample. Note that the sample name in the annotation file must match the non-suffix part of the pathoscope file name. For example, one pathoscope report filename is: “sample_011-sam-report.tsv”, then the corresponding sample name in the annotation file must be: “sample_011”.
Instructions:
Also, make sure to provide the correct column number for sample name in the annotation file, as well as the annotation file separator (tab, comma or semicolon).
Note: One example pathoscope eport table and the annotation table will show up in the right panel.
Running time:
animalcules-id is a separate R pipeline that generates pathoscope-like outputs from fastq files. The required input is the animalcules-id generated .rds file. Here users could choose either EM count assay or Best hit assay.
Running time: < 1s