I think I got stuck at the last step of this strategy when trying to analyze a number of feature (pathways) from 6 datasets using the tutorial here
The initial table after df.weights %>% full_join(abs.weights) has 77,879 rows.
Here is sample of this initial dataframe , it contains a lot of zeros in the 'median.rel.weight' with the exception of those 22 values maintained in this table.
df.weights %>%
full_join(abs.weights) %>%
# normalize by the absolute model size
mutate(median.rel.weight=median.rel.weight/sum.median) %>%
# only include genera of interest
filter(feature %in% feat.of.interest$feature)
This would be the initial filtering of the feat.of.interest also present in the df.weights matrix. This returns 4,409 rows.
df.weights %>%
full_join(abs.weights) %>%
# normalize by the absolute model size
mutate(median.rel.weight=median.rel.weight/sum.median) %>%
# only include genera of interest
filter(feature %in% feat.of.interest$feature) %>%
# highlight feature rank for the top 20 features
mutate(r.med=case_when(r.med > 10~NA_real_, TRUE~r.med))
This also returns 4,409 rows so the mutate(r.med=case_when(r.med > 1~NA_real_, TRUE~r.med)) part seems to have no effect.
Could you help explaining me this? At the moment the code seems to plot a lot of rows way more than 10-20 and I can clearly see 7 features highlighted with numbers in the heatmap but there doesn't seem to be a filter in action (this also happened with taxa, with metaphlan output). Is this due to the abundance of '0' in the df.weights$median.rel.weight column?
Dear all,
I think I got stuck at the last step of this strategy when trying to analyze a number of feature (pathways) from 6 datasets using the tutorial here
The initial table after
df.weights %>% full_join(abs.weights)
has 77,879 rows.Here is sample of this initial dataframe , it contains a lot of zeros in the 'median.rel.weight' with the exception of those 22 values maintained in this table.
This would be the initial filtering of the feat.of.interest also present in the df.weights matrix. This returns 4,409 rows.
This also returns 4,409 rows so the
mutate(r.med=case_when(r.med > 1~NA_real_, TRUE~r.med))
part seems to have no effect.Here is the table after the filter above just before the mutate step, saved as a tsv.
Could you help explaining me this? At the moment the code seems to plot a lot of rows way more than 10-20 and I can clearly see 7 features highlighted with numbers in the heatmap but there doesn't seem to be a filter in action (this also happened with taxa, with metaphlan output). Is this due to the abundance of '0' in the df.weights$median.rel.weight column?
Thanks very much for the help
Andrés