I think the issue here is you need to permute the data to NumFeatures x BatchSize for featureInputLayer when working with trainNetwork and datastores. Here's an example:
% Create fake data with 100 observations of 5 features and 1 target
xy = randn(100,6);
% split off features from targets
x = xy(:,1:5);
y = xy(:,6);
% permute for featureInputLayer
% For featureInputLayer and trainNetwork using datastores the data has to
% be shaped as NumFeatures x BatchSize
x = x.';
y = y.';
% Create datastores with cell outputs, and iterate along dimension 2 (since
% we permuted the data it is now in NumFeatures x BatchSize).
xds = arrayDatastore(x,"OutputType","cell","IterationDimension",2);
yds = arrayDatastore(y,"OutputType","cell","IterationDimension",2);
% Combine the datastores
cds = combine(xds,yds);
% Create a simple network and train.
layers = [featureInputLayer(5)
fullyConnectedLayer(1)
regressionLayer];
opts = trainingOptions("adam");
net = trainNetwork(cds,layers,opts);
