List all species present in colorectal cancer datasets
Levi Waldron
CUNY Graduate School of Public Health and Health Policylevi.waldron@sph.cuny.edu
27 August 2025
Source:vignettes/identify_CRC_species.Rmd
identify_CRC_species.RmdOverview
This analysis:
- selects a subset of samples (all CRC-related in this example),
- uses the table1 package to display a table of the characteristics of the included cohort,
- sorts species in order of descending prevalence,
- uses the DT package
datatablefunction to display a searchable, paged table of prevalences, and - writes the prevalences to file.
Required packages:
Select samples
Note that a few species without phylogenetic information are lost.
crc_subset <- filter(sampleMetadata, study_condition == "CRC") %>%
returnSamples(dataType = "relative_abundance",
rownames = "short")Cohort characteristics
Create a summary table of the participants in this cohort:
table1::table1( ~ disease + disease_subtype + age + gender + country + study_name,
data = colData(crc_subset))| Overall (N=701) |
|
|---|---|
| disease | |
| CRC | 625 (89.2%) |
| CRC;cholesterolemia | 1 (0.1%) |
| CRC;fatty_liver | 3 (0.4%) |
| CRC;fatty_liver;hypertension | 12 (1.7%) |
| CRC;hypercholesterolemia | 3 (0.4%) |
| CRC;hypercholesterolemia;hypertension | 1 (0.1%) |
| CRC;hypertension | 20 (2.9%) |
| CRC;metastases | 1 (0.1%) |
| CRC;T2D | 29 (4.1%) |
| CRC;T2D;fatty_liver;hypertension | 4 (0.6%) |
| CRC;T2D;hypertension | 2 (0.3%) |
| disease_subtype | |
| adenocarcinoma | 59 (8.4%) |
| carcinoma | 173 (24.7%) |
| Missing | 469 (66.9%) |
| age | |
| Mean (SD) | 63.3 (11.0) |
| Median [Min, Max] | 64.0 [28.0, 90.0] |
| gender | |
| female | 257 (36.7%) |
| male | 444 (63.3%) |
| country | |
| AUT | 46 (6.6%) |
| CAN | 2 (0.3%) |
| CHN | 74 (10.6%) |
| DEU | 60 (8.6%) |
| FRA | 53 (7.6%) |
| IND | 30 (4.3%) |
| ITA | 61 (8.7%) |
| JPN | 298 (42.5%) |
| USA | 77 (11.0%) |
| study_name | |
| FengQ_2015 | 46 (6.6%) |
| GuptaA_2019 | 30 (4.3%) |
| HanniganGD_2017 | 27 (3.9%) |
| ThomasAM_2018a | 29 (4.1%) |
| ThomasAM_2018b | 32 (4.6%) |
| ThomasAM_2019_c | 40 (5.7%) |
| VogtmannE_2016 | 52 (7.4%) |
| WirbelJ_2018 | 60 (8.6%) |
| YachidaS_2019 | 258 (36.8%) |
| YuJ_2015 | 74 (10.6%) |
| ZellerG_2014 | 53 (7.6%) |
Show species in order of decreasing prevalence
Prevalences shown are the fraction of specimens from CRC patients with non-zero relative abundance.
prevalences <- rowSums(assay(crc_subset) > 0) / ncol(crc_subset)
prevalences <- tibble(species = names(prevalences), prevalence = signif(prevalences, 2)) %>%
filter(prevalence > 0) %>%
arrange(-prevalence)
DT::datatable(prevalences)Write to disk:
write.csv(prevalences, row.names = FALSE, file = "prevalences.csv")Download the zipped prevalences file produced by curatedMetagenomicData version 3.2.3: prevalences.csv.zip
Session Info
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