getDA | R Documentation |
Inspect the list of p-values or/and log fold changes from the output of a differential abundance detection method.
getDA( method, slot = "pValMat", colName = "rawP", type = "pvalue", direction = NULL, threshold_pvalue = 1, threshold_logfc = 0, top = NULL, verbose = FALSE )
method |
Output of a differential abundance detection method.
|
slot |
The slot name where to extract values
(default |
colName |
The column name of the slot where to extract values
(default |
type |
The value type of the column selected where to extract values.
Two values are possible: |
direction |
|
threshold_pvalue |
Threshold value for p-values. If present, features
with p-values lower than |
threshold_logfc |
Threshold value for log fold changes. If present,
features with log fold change absolute values higher than
|
top |
If not null, the |
verbose |
Boolean to display the kind of extracted values
(default |
A data.frame
with several columns:
stat
which contains the p-values or the absolute log fold
change values;
direction
which is present if method
was a
data.frame
object, it contains the information about
directionality of differential abundance (usually log fold changes);
DA
which can contain several values according to
thresholds and inputs. "DA"
or "non-DA"
if method
object was a vector, "UP Abundant"
, "DOWN Abundant"
, or
"non-DA"
if method
was a data.frame
.
getStatistics
, extractDA
data("ps_plaque_16S") # Add scaling factors ps_plaque_16S <- norm_edgeR(object = ps_plaque_16S, method = "TMM") # DA analysis da.limma <- DA_limma( object = ps_plaque_16S, design = ~ 1 + HMP_BODY_SUBSITE, coef = 2, norm = "TMM" ) # features with p-value < 0.1 as DA getDA( method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue", direction = NULL, threshold_pvalue = 0.1, threshold_logfc = 0, top = NULL ) # top 10 feature with highest logFC are DA getDA( method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue", direction = "logFC", threshold_pvalue = 1, threshold_logfc = 0, top = 10 ) # features with p-value < 0.1 and |logFC| > 1 are DA getDA( method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue", direction = "logFC", threshold_pvalue = 0.1, threshold_logfc = 1, top = NULL ) # top 10 features with |logFC| > 1 are DA getDA( method = da.limma, slot = "pValMat", colName = "rawP", type = "pvalue", direction = "logFC", threshold_pvalue = 1, threshold_logfc = 1, top = 10 )
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