Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods.

Resolution of Customer Return Non-Volatile Memory Data Retention Bit Failures through Bit Map Verification and Bit Cell Characterization by Nanoprobe Analysis

A major customer had been returning devices for nonvolatile memory (NVM) data retention bit failures. The ppm level was low but the continued fallout at the customer location was causing a quality and reliability concern. The customer wanted a resolution as to the cause of the failures and for a corrective action. An NVM bit data retention failure occurs when a programmed bit loses it programmed data state over time and flips to the opposite data state (0 -> 1 or 1 -> 0) causing a programming error. Previous failure analysis results on several failing devices with a single NVM bit data retention failure was inconclusive. TEM analysis showed no difference between the failing bit and neighboring passing bit. The lack of results led to the questioning of the accuracy of the bit map documentation and if the TEM analysis was being performed at the correct bit location. Bit map documentation takes the failing bit's electrical address and converts it to a physical address location. If the bit map documentation is incorrect, locating the failing bit is not possible and physical failure analysis will not be performed at the correct bit location. This paper will demonstrate how Atomic Force Probe (AFP) nanoprobe analysis was used to first verify the bit map documentation by determining the programming of bits at specific locations through bit cell characterization; and then characterize the failing bit location to verify the programming error and determine the possible failure mechanism based on its electrical signature followed by the appropriate physical analysis to determine the failure mechanism.