I was able to concatenate my loom objects into 1 object and converted it to Seurat object, but this object has not been in anyway processed (e.g., normalise etc that are typically done in scVelo pipeline). How might I add the ambiguous, matrix, spliced, unspliced to my Seurat GEX clus...
Notes: (i) arrow plot is normalise to one, so arrows only show flow direction, not the velocity magnitude, and (ii) velocity magnitudes are indicated by the colour scale and (iii) velocity magnitudes larger than 5.0 × 10−7 (m/s) are not shown for clarity (these velocities occur in...
UserKNN:a classicuser neighbourhood-basedCFapproach using the Cosine Similarity to find like-minded users. ItemKNN: anitem neighbourhood-basedvariant of UserKNN. RP3beta:a simple yet effectivegraph-basedmethodwhich performs a random walk between users and items based on the observed interaction matr...
where Y are the functional observations, X is a design matrix, \beta the parameter vector and \epsilon the error of the model. Let b be the estimated parameters. The design matrix X can be subdivided into X = [X_1 | X_2], where X_1 denotes the covariates of interest and X_2 the...
# NORMALISE SUBSTANCES AND CALCULATE NORMALISE MEANS # Script to normalise substance concentrations and calculate means. # This script normalises each replicate time series based on the maximum, calculates mean concentrations for each substance at each timepoint # from all replicates, gen...
Here data is20x10. Now we make a random guess as to what the parameters are, prior1 = normalise(rand(Q,1)); %初始状态概率 transmat1 = mk_stochastic(rand(Q,Q)); %初始状态转移矩阵 obsmat1 = mk_stochastic(rand(Q,O)); %初始观测状态到隐藏状态间的概率转移矩阵 ...
2. Normalise Case After converting your data into tokens, the next step is to standardise your case into lowercase so that a machine can recognize the same words. While there are some instances, (like names “Bush”vs. “bush”) where you may lose context, lowercasing tokens are a simple...
we can always add extra hidden nodes to represent the current "regime", thereby creating mixtures of models to capture periodic non-stationarities.There are some cases where the size of the state space can change over time, e.g., tracking a variable, but unknown, number of objects.
transmat0, obsmat0, nex, T); Here data is 20x10. Now we make a random guess as to what the parameters are, prior1 = normalise(rand(Q,1));transmat1 = mk_stochastic(rand(Q,Q));obsmat1 = mk_stochastic(rand(Q,O));and improve our guess using 5 iterations of EM...
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