See the reference documentation for more details.
diff --git a/docs/namespacesinglepp.html b/docs/namespacesinglepp.html index a589796..b55e7ab 100644 --- a/docs/namespacesinglepp.html +++ b/docs/namespacesinglepp.html @@ -238,10 +238,10 @@A vector of vectors of ranked marker lists, used to determine which genes should be used to compute correlations in Classifier.
For a Markers object markers, let us consider the vector at markers[0][1]. This vector is expected to contain the ranked indices of the marker genes for label 0 compared to label 1. Typically, this vector is created by identifying the genes that are upregulated in label 0 compared to 1 and sorting by decreasing effect size. Indices should refer to the rows of the reference expression matrices (i.e., ref in train_single()). So, for example, markers[0][1][0] should contain the row index of the most upregulated gene in label 0 compared to 1.
A vector of vectors of marker lists, with one list for each pairwise comparison between labels in the reference dataset. This is used to determine which genes should be used to compute correlations in train_single() and train_single_intersect().
For a Markers object markers, let us consider the vector at markers[0][1]. This vector should contain a list of marker genes for label 0 compared to label 1. Each gene is represented as the row index of the reference expression matrix, i.e., ref in train_single() and train_single_intersect(). The vector should also be sorted by the "strength" of the markers such that the earliest entries are the strongest markers for that pairwise comparison. Typically, this vector is created by identifying the genes that are upregulated in label 0 compared to 1 and sorting by decreasing effect size. So, for example, markers[0][1][0] should contain the row index of the most upregulated gene in this comparison.
For a given reference dataset, the corresponding Markers object should have length equal to the number of labels in that reference. Each middle vector (i.e., markers[i] for non-negative i less than the number of labels) should also have length equal to the number of labels. Any innermost vector along the "diagonal" (i.e., markers[i][i]) is typically of zero length. The innermost vectors that are not on the diagonal (i.e., markers[i][j] for i != j) may be of any positive length and should contain unique row indices. Note that the length of all innermost vectors will be be capped by any non-negative TrainSingleOptions::top in train_single() and friends.
As mentioned previously, the diagonal innermost vectors are typically empty, given that it makes little sense to identify upregulated markers in a label compared to itself. That said, any genes stored on the diagonal will be respected and used in all gene subsets for the corresponding label. This can be exploited by advanced users to efficiently store "universal" markers for a label, i.e., markers that are applicable in all comparisons to other labels.
+As mentioned above, the diagonal innermost vectors are typically empty, given that it makes little sense to identify upregulated markers in a label compared to itself. That said, any genes stored on the diagonal will be respected and used in all gene subsets for the corresponding label. This can be exploited by advanced users to efficiently store "universal" markers for a label, i.e., markers that are applicable in all comparisons to other labels.
classify_integrated(). classify_integrated(). ref, containing the label for each reference profile. Labels should be integers in \([0, L)\) where \(L\) is the total number of unique labels. Markers for more details. singlepp::Markers for more details. intersect_genes() for more details. ref, containing the label for each reference profile. Labels should be integers in \([0, L)\) where \(L\) is the total number of unique labels. Markers for more details. singlepp::Markers for more details. ref, containing a gene identifier for each row of the reference dataset. Identifiers should be comparable to those in test_id. If any duplicate IDs are present, only the first occurrence is used. ref, containing the label for each reference profile. Labels should be integers in \([0, L)\) where \(L\) is the total number of unique labels. Markers for more details. singlepp::Markers for more details.