Select Maker 0.97 Beta 8
Clotilde Wilks
Methods: A Mendelian randomization study was performed on summary-level data from the largest published genome-wide association studies. Single nucleotide polymorphisms with a genome-wide significance level were selected as instrumental variables for leukocyte telomere length (LTL), and serum soluble makers of inflammation (CRP, IL-6, TNF-α, and IGF-1). Standard inverse variance weighted (IVW) method was used as the primary statistical method. The weighted median, MR-Egger regression, and MR-PRESSO methods were used for sensitivity analysis.
NS-Forest version 2.0 workflow. The method begins with a cell-by-gene expression matrix with cluster assignments for each cell (A). This clustered expression matrix is used to generate binary classification models for each cell cluster using the random forest machine learning method. Features are extracted from the model and ranked by Gini Index (B). Top features are filtered by expression level to remove negative markers (C) before being reranked by Binary Expression Score (D,E). Decision branch expression level cutoffs are derived from decision tree analysis for the most binary features (F) and F-beta score used as an objective function to evaluate the discriminatory power of all permutations of selected markers (G).
Comparison of Hodge et al. (2019) markers with NS-Forest v2.0 for the full MTG. (A) Unscaled heat map for both sets of markers where the values are the mean expression per gene. (B) F-beta scores (y-axis) for the single Hodge marker gene (blue), the best NS-Forest single marker gene (orange), and the combination of marker genes found by NS-Forest (gray). (C) An example violin plot of a binary expression pattern selected by the method used by Hodge et al. (2019) for cluster Exc_L2_4_LINC00507_GLP2R, with expression given as log2 CPMs. For all panels, cell type clusters are listed along the x-axis in taxonomic order. (D) Taxonomy ordered labels corresponding to the x-axis of the heat maps in A and also the violin plot in C.
One major difference between these two approaches is that the Hodge marker set contains a single marker per cluster, selected to label a distinct cluster phenotype, whereas NS-Forest selects combinations of markers that optimize classification power. By running the Hodge markers through NS-forest v2.0, we estimated F-beta scores for the single Hodge markers in order to compare their classification power to the best single NS-Forest markers and the NS-Forest marker combinations (Fig. 5B). Overall, the trend lines show that the F-beta scores for single markers (blue and orange lines) follow a similar trajectory, with some clusters being more difficult to classify than others, that is, having lower F-beta scores. However, the NS-Forest marker combinations (gray line) provide a uniformly higher power of discrimination over either single marker, regardless of how the single best marker is chosen.
Clustering was also performed after removal of the monocytes, which resulted in six clusters corresponding to the DC1-DC6 types as characterized in the original study (Fig. 7C). For the six clusters produced by reprocessing these data, NS-Forest v2.0, COMET, and RANKCORR required 9, 12, and 19 markers, respectively, to produce optimal classification results (Fig. 7D). Three markers were shared by all methods and four markers shared between NS-Forest v2.0 and COMET and between NS-Forest v2.0 and RANKCORR. Again, NS-Forest v2.0 outperformed both COMET and RANKCORR, but the F-beta score results were more comparable with these clusters. The median F-beta scores were [0.95 > 0.89 > 0.86] for NS-Forest v2.0 > RANKCORR > COMET, and the average Binary Scores were [0.979 > 0.978 > 0.78] for NS-Forest v2.0 > RANKCORR > COMET.
The F-score is the harmonic mean of precision and recall providing equal weight for these two classification measures. The F-beta score includes a beta term that allows for the weighting of the function toward either precision (beta < 1) or recall (beta > 1) (Fig. 1G). The beta for the analysis described here was estimated empirically at 0.5. In brief, the empirical selection of 0.5 was based on a balance of the average values for the confusion matrix across all cell type clusters while varying the beta parameter. At a beta of 0.5, there was an optimum reached in the confusion matrix while averaging approximately two markers per cell type cluster (Supplemental Fig. S13). This parameter should be evaluated for each data set, as it adjusts for the amount of zero inflation within the data. Here, we are analyzing Smart-Seq data which are known to have comparatively lower zero inflation versus droplet-based methodologies.
I faced the same problem as in v.0.97.3_beta in Win7 64-bit, but in this 099 beta version the 64-bit error wouldn't resolve despite the recommendations given at =5 (x64 Fix by linkhyrule5 - February 22, 2011 - 5:34pm).
Could you please share again a NEW x64 FIX without that trouble changing the code in "Portable App Creator.au3" as the code itsef has been changed (as far as I understood). May be it's reasonable to include a new version of Regshot (eg. 2.0.1.70 unicode x64 which works in x32 either) in the compiled distributive of Portable App Creator?
Thanx a lot!
Please could you download 0.97.2, this fixes an issue selecting a bitrate from the dropdown list, on restarting the program it is reset back to 128, this started in 0.97 when i added the ability to type in a value. Other beta features have been added in 0.97.1 and 0.97.2, see the change log on the post.
I have updated to 0.97.1, you can now select .opus or .ogg in the File Extension dropdown box, I added quickly because if you forget to change the extensions back before syncing later the software would see the .ogg files as extra files and delete them and you would have to start again. Hope this change helps
beta_constraints: (GLM) To use beta constraints, select a dataset from the drop-down menu. The selected frame is usedto constraint the coefficient vector to provide upper and lower bounds.
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