matlab function lsqnonneg  (MathWorks Inc)

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Main menu selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Data file selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Shuffled data file selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Histogram of angle differences between the original and shuffled data. Created with MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques:

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Angles data file selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Hierarchical clustering dendrogram. Created with MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques:

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Divergence analysis source file selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Convergence analysis source file selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Somatic analysis data file selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection

Journal: Neural Regeneration Research

Article Title: A novel method for clustering cellular data to improve classification

doi: 10.4103/NRR.NRR-D-24-00532

Figure Lengend Snippet: Non-negative least squares analysis data file selection screen. Screenshot from MATLAB.

Article Snippet: Data are scaled, where each value is divided by the number of regions and multiplied by the number of clusters, such that the sum of all values is equal to the number of clusters and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__scaled_yyyymmddHHMMSS.xlsx.” c. Data are normalized by column and saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__column_normalized_yyyymmddHHMMSS.xlsx.” d. Tract values are normalized, and the final bi-normalization data are saved to a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__bi_normalized_yyyymmddHHMMSS.xlsx.” e. The resulting vector x, where x is the k-dimensional vector representing the fractions of neurons in each neuronal class, is saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__X_vector_yyyymmddHHMMSS.xlsx.” f. The squared Euclidean norm of the residual of the MATLAB function lsqnonneg() is calculated and the result saved in a file named in the manner of “nnls__PRE_matrix__axonal_counts_per_parcel_per_cluster_and_tract_values__residual_norm_yyyymmddHHMMSS.xlsx.” vi.

Techniques: Selection