Feature Counts
Feature Counts - Related Videos
10:19
Using a Hemacytometer to Count Cells
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2023
Many biomedical experiments require manipulation of a known quantity of cells, in order to achieve accurate, reproducible, and statistically-relevant data. Therefore, learning how to count cells is a particularly essential technique for any successful biomedical scientist. The most common way to count cells is by using a hemacytometer - an instrument that bears two laser-etched grids, which aid in the enumeration of an aliquot cells under a simple light microscope. This data can then be used to...
11:25
Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
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Cited by 22 •
2009
The anatomical organization of the primate brain can provide important insights into normal and pathological conditions in humans. Unbiased stereology is a method for accurately and efficiently estimating the total neuron number (or other cell type) in a given reference space1.
05:47
Counting and Determining the Viability of Cultured Cells
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Cited by 19 •
2008
Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. In this video, we demonstrate how cells are counted using a hemacytometer.
11:01
Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
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Cited by 90 •
2016
This paper describes the quantification of hemocytometer and migration/invasion micrographs through two new open-source ImageJ plugins Cell Concentration Calculator and migration assay Counter. Furthermore, it describes image acquisition and calibration protocols as well as discusses in detail the input requirements of the plugins.
06:37
Counting Human Neural Stem Cells
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Cited by 14 •
2007
Knowledge of the exact number of viable cells is required for many tissue culture manipulations. This protocol describes how to differentiate between live and dead cells and quantify cells using a hemacytometer. Although it describes counting human neural stem/precursor cells (hNSPCs), it can be used for other cell types.
FAQs
Assignment rules determine which reads are attributed to annotated genes or transcripts when reads overlap one or more features. Pipelines apply defined criteria based on the relationship between mapped reads and feature annotations, so changing those criteria can alter the resulting tallies. Consistent rules are therefore important when comparing samples or interpreting differences in measured expression.
The reference provides the coordinate or sequence framework used to relate sequencing reads to annotated features. A genome-based workflow can assign reads in a genomic context, whereas a transcriptome-based workflow uses annotated transcript sequences. Because the reference and annotation determine which features are available for assignment, they directly shape the resulting feature-level measurements.
A count matrix organizes numerical tallies so that features, such as genes or transcripts, can be compared across biological samples. Its structure makes sample-to-sample expression patterns accessible to downstream analysis while retaining the feature identities used during assignment. This organization supports statistical modeling, differential expression analysis, and broader interpretation of coordinated biological responses.
A typical workflow begins with raw sequencing reads and uses computational methods to align or map them to a reference genome or transcriptome. The pipeline then compares mapped reads with annotated features, applies its overlap and assignment rules, and records the resulting tallies in a count matrix. This sequence connects raw data with interpretable measurements for later analysis.
Researchers use these measurements when they need to compare gene or transcript activity across samples. Count matrices can support identification of differentially expressed features, reveal changes associated with biological responses, and contribute to cell-type classification. In this way, the same counting output can support both focused comparisons between conditions and broader studies of sample composition.
Reliable counting provides a reproducible numerical foundation for statistical analyses of sequencing experiments. Once features are consistently assigned and organized across samples, researchers can model expression differences and examine patterns involving many genes or transcripts together. Those results can then inform systems biology investigations, where coordinated feature behavior helps characterize broader biological responses rather than isolated measurements.