1. A method for analyzing multimodal data derived from a biological sample, comprising: (a) inputting, via a computing device, the multimodal data comprising a plurality of data; (b) generating, via the computing device, a plurality of quantitative descriptors in the multimodal data; (c) constructing, via the computing device, a higher dimensional multi-parametric feature space using the plurality of quantitative descriptors; (d) reducing, via the computing device, the higher dimensional multi-parametric feature space to a lower dimensional ensemble embedding by an ensemble embedding method comprising: (i) generating, via the computing device, a plurality of base embeddings derived from the higher dimensional multi-parametric feature space, wherein the plurality of base embeddings comprises comprising weak embeddings and strong embeddings; (ii) calculating, via the computing device, an embedding strength for each base embedding; (iii) selecting, via the computing device, a plurality of the strong embeddings, wherein the embedding strength of each strong embedding is greater than a pre-specified threshold value; (iv) constructing, via the computing device, the lower dimensional ensemble embedding by: (1) quantifying, via the computing device, a plurality of pair-wise relationships for all quantitative descriptors in each strong embedding in step (iii) via a Euclidean pair-wise distance method; (2) normalizing, via the computing device, the plurality of pair-wise relationships to yield a plurality of normalized relationships; and (3) propagating, via the computing device, the plurality of normalized relationships by a projection method; and (4) obtaining, via the computing device, the lower dimensional ensemble embedding. (v) optimizing, via the computing device, the generating a plurality of independent embeddings step (i), the calculating an embedding strength step (ii), the selecting a plurality of strong embeddings step (iii), and the constructing the lower dimensional ensemble embedding step (iv); and (e) obtaining, via the computing device, a classification for the biological sample based on the ensemble embedding; and (f) outputting, via the computing device, the classification for the biological sample.