Description Usage Arguments Value Author(s) References Examples
This function returns a countFilter object, performing all of the calculations needed to filter the data based off a specified function and limit
1 | count_based_filter(omicsData, fn = "sum", group = FALSE, group_var = NULL)
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omicsData |
An object of one of the classes "seqData" |
fn |
Specify "mean" to use the mean count of each OTU, "percent" to use mean counts lower than a certain percent, "max" to use the max count across all samples, "sum" to use the total count of each OTU, "nonmiss" to use presence/absence counts, or "ka" to use k over a filtering (need at least k counts of OTUs seen in at least a samples). |
group |
Logical, should filtering function be performed separately for specified groups. Default is FALSE. |
group_var |
Character, if filtering should be performed separately for specified groups, then specify which grouping variable to use. If group = TRUE and group_var = NULL, will use 'Group' from attr(omicsData, "group_DF"). Default is NULL. |
An object of class countFilter (also a data.frame) that contains the molecule identifier and the mean/percent/max/sum/nonmissing count across all samples.
Allison Thompson, Bryan Stanfill
Arumugam, Manimozhiyan, et al. "Enterotypes of the human gut microbiome." nature 473.7346 (2011): 174-180.
https://bioinformatics.oxfordjournals.org/content/early/2013/07/15/bioinformatics.btt350.full
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ## Not run:
library(mintJansson)
data(rRNA_data)
omicsData <- rRNA_data
#Find mean count of OTUs
mean_lim <- count_based_filter(omicsData, fn="mean")
head(mean_lim)
summary(mean_lim)
plot(mean_lim)
#Find percentage of each OTU
perc_lim <- count_based_filter(omicsData, fn="percent")
head(perc_lim)
summary(perc_lim)
plot(perc_lim)
#Find maximum count of OTUs
max_lim <- count_based_filter(omicsData, fn="max")
head(max_lim)
summary(max_lim)
plot(max_lim)
#Find total count of OTUs
sum_lim <- count_based_filter(omicsData, fn="sum")
head(sum_lim)
summary(sum_lim)
plot(sum_lim)
#Find number of nonmissing OTUs
nonmiss_lim <- count_based_filter(omicsData, fn="nonmiss")
head(nonmiss_lim)
summary(nonmiss_lim)
plot(nonmiss_lim)
#Find order of samples for k/a filtering
ka_lim <- count_based_filter(omicsData, fn="ka")
head(ka_lim)
summary(ka_lim)
plot(ka_lim)
## End(Not run)
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