Stability condition for the drive bunch in a collinear wakefield accelerator

The beam breakup instability of the drive bunch in the structure-based collinear wakefield accel- erator is considered and a stabilizing method is proposed. The method includes using the specially designed beam focusing channel, applying the energy chirp along the electron bunch, and keeping energy chirp constant during the drive bunch deceleration. A stability condition is derived that defines the limit on the accelerating field for the witness bunch.Comment: 10 pages, 6 figure

Example layout of expression data to be used as input for ROTS, where columns represent different samples and rows represent the features.

<p>Example layout of expression data to be used as input for ROTS, where columns represent different samples and rows represent the features.</p

Visualizations provided by ROTS.

<p>(A) Volcano plot of the features, where the differentially expressed features are coloured red. (B) MA plot of the features, where the differentially expressed features are coloured red. (C) ROTS reproducibility Z-score as function of top list size. The highest score is marked with red dot together with its value. (D) Histogram of <i>p</i>-values. (E) Principal component analysis (PCA) plot of the differentially expressed features. (F) Heatmap and hierarchical clustering of the samples (columns) and the differentially expressed features (rows) using euclidean distance and the complete-linkage agglomerative clustering method.</p

Precision, recall, and false positive ratios of ROTS and current state-of-the-art methods for single-cell RNA-seq in the innate lymphoid cell data.

<p>(A) Precision of the findings in reduced data. Precision was defined as the ratio between the number of common detections in the reduced and full data, and the total number of detections in the reduced data. Median values over ten randomly generated subsets are indicated by lines across the different numbers of cells per group. (B) Recall of the findings in reduced data. Recall was defined as the ratio between the number of common detections in the reduced and full data, and the total number of detections in the full data. Median values over ten randomly generated subsets are indicated by lines across the different numbers of cells per group. (C) False positive ratios of the findings in ten randomly generated mock datasets. The false positive ratio was defined as the ratio between the number of differentially expressed genes in the mock comparison and the average number of differentially expressed genes in the actual comparison. Limma was visualized separately because of the different scale compared to the other methods and jittering was used to separate overlapping points.</p

Cross-Correlation of Spectral Count Ranking to Validate Quantitative Proteome Measurements

The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass

Optimization of Statistical Methods Impact on Quantitative Proteomics Data

As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards as well as “real” experiments where differences in protein abundance are not known a priori. Our results suggest that data-driven reproducibility-optimization can consistently produce reliable differential expression rankings for label-free proteome tools and are straightforward in their application