1. A computer implemented method of determining, for each respective phenotypic characterization in a set of {T 1 ,. .. , T k } phenotypic characterizations, a probability that a test biological specimen has the respective phenotypic characterization, the method comprising: (A) learning a pairwise probability function g pq (X, W pq ) using a training population, for a pair of phenotypic characterizations (T p , T q ) in the set of {T 1 ,. .. , T k } phenotypic characterizations, wherein (i) there are at least five training samples in the training population for each phenotypic characterization in the set of {T 1 ,. .. , T k } phenotypic characterizations; (ii) Y is the set of all training samples in the training population that exhibits either phenotypic characterization T p or phenotypic characterization T q , and each Y i in Y is the set of {y i1 ,. .. , y in } cellular constituent abundance values for a plurality of cellular constituents measured from a sample i, from the training population, which exhibits either phenotypic characterization T p or phenotypic characterization T q ; (iii) W pq is a set of parameters derived from Y in the learning step (A) for a pair of phenotypic characterizations (T p , T q ) by substituting each Y i into g pq (X, W pq ), as X, during said learning step (A); (iv) k is 3 or greater; (v) n is at least 1; and (vi) p is not equal to q; (B) repeating the learning step (A) for a different pair of phenotypic characterizations (T p , T q ), using the training population, for all unique pairs of phenotypic characterizations in the set of {T 1. .. , T k } phenotypic characterizations, thereby deriving a plurality of pairwise probability functions G={g 1,2 (X, W 1,2 ),. .. , g k-1, k (X, W k-1, k )}; (C) computing a plurality of pairwise probability values P={p 1,2 ,. .. , p k-1, k }, wherein each pairwise probability value p pq in P is equal to g pq (Z, W pq ) in G, the probability that the test biological specimen has phenotypic characterization T p and does not have phenotypic characterization T q , wherein Z is a set of {z 1 ,. .. , z n } cellular constituent abundance values measured from the test biological specimen for said plurality of cellular constituents; (D) optionally converting P to a set M of k probabilities, wherein M={p 1 , p 2 ,. .. , p k }, wherein each probability p j in M is a probability for a phenotypic characterization in the set of {T 1 ,. .. , T k } phenotypic characterizations that the test biological specimen has the phenotypic characterization such that ∑ j = 1 k ⁢ p j is equal to a predetermined constant; and (E) outputting one or more pairwise probabilities p pq in P to a user, a user interface device, a monitor, a computer readable storage medium, or a local or remote computer system when step (D) is not performed; or displaying one or more pairwise probabilities p pq in P when step (D) is not performed, or outputting one or more p j in M and/or one or more pairwise probabilities p pq in P to a user, a user interface device, a monitor, a computer readable storage medium, or a local or remote computer system when step (D) is performed; or displaying one or more p j in M and/or one or more pairwise probabilities p pq in P when step (D) is performed.