Description Usage Arguments Value Author(s) See Also Examples
log2_FC
extends the results obtained via distinct_test
, by computing fold changes (FC) and log2-FC between conditions.
1 2 3 4 5 6 7 |
res |
a |
x |
a |
name_assays_expression |
a character ("cpm" by default), indicating the name of the assays(x) element which stores the expression data (i.e., assays(x)$name_assays_expression). We strongly encourage using normalized data, such as counts per million (CPM). Do not use logarithm transformed data to compute FCs. |
name_group |
a character ("group_id" by default), indicating the name of the colData(x) element which stores the group id of each cell (i.e., colData(x)$name_group). |
name_cluster |
a character ("cluster_id" by default), indicating the name of the colData(x) element which stores the cluster id of each cell (i.e., colData(x)$name_cluster). |
A data.frame
object, extending the results in 'res'.
Two additional columns are added: FC_group1/group2 and log2FC_group1/group2, inicating the FC and log2-FC of group1/group2.
A FC > 1 (or log2FC > 0) indicates up-regulation of group1 (compared to group2); while a FC < 1 (or log2FC < 0) indicates down-regulation of group1 (compared to group2).
Simone Tiberi simone.tiberi@uzh.ch
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # load pre-computed results (obtaines via `distinct_test`)
data("res", package = "distinct")
# load the input data:
data("Kang_subset", package = "distinct")
# We can optionally add the fold change (FC) and log2-FC between groups:
res = log2_FC(res = res,
x = Kang_subset,
name_assays_expression = "cpm",
name_group = "stim",
name_cluster = "cell")
# Visualize significant results:
head(top_results(res))
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