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# Release notes for Cell Ranger 6.1.2 (October 25, 2021):

## Bug Fixes

1. Improved handling of memory requests for large genomes.
2. Fixes an issue in the molecule_info.h5 file, where if a species was not present in a barnyard run, it was omitted from the genome information.
3. Reduces the size of the bundled reference included in cellranger testrun.
4. Adds additional guidance in the web summary when there is a low fraction of targeted genes enriched.
5. Fixes a metric issue where aggregate antibodies could be double-counted.
6. Unsets additional sysconfig environment variables prior to pipeline execution, which may otherwise interfere with the pipeline conda environment.

# Release notes for Cell Ranger 6.1.1 (August 12, 2021):

## Bug Fixes

1. Fix an issue where cellranger vdj could fail if executed by a user without a home directory.

# Release notes for Cell Ranger 6.1.0 (August 9, 2021):

## New Feature: High Throughput (HT) for Chromium X

1. Cell Ranger 6.1 introduces support for the 3' and 5' High Throughput (HT) kits with 16 channels per chip, allowing users to process 2,000-20,000 cells per channel (3' and 5') or 2,000-60,000 cells per channel with CellPlex (3' only). HT kits are only compatible with Chromium X, which is backwards compatible with all 10x Genomics dual indexed assays. For more information see What is HT?
2. Cell Ranger 6.1 includes a new throughput detection algorithm to detect HT samples in 3' CellPlex data as described in the CellPlex algorithms page. In the event that chemistry detection fails, it can be overridden with the option (e.g., --chemistry=SC3Pv3HT) in cellranger count, or in the cellranger multi csv file to detect HT samples when 3' CellPlex libraries are run.
3. Minor changes to web_summary.html include a new alert when the user specifies --chemistry=SC3Pv3HT, but Cell Ranger detects otherwise. HT will be appended to the detected chemistry if the pipestance was a multiplexing run.
4. Note that with HT chemistry and 3' CellPlex it is now possible to run 60,000 cells per GEM well, and aggregate million-cell datasets. Larger datasets will require additional memory beyond our stated minimum requirement of 64 GB. See the 3' system requirements page for more details and time trial data.

## General Improvements

1. Numerous performance optimizations have been made, especially for pipeline stages that iterated over molecule_info.h5 files, such as cellranger aggr, but also memory allocation improvements. We have seen up to 2-3x speed improvements for cellranger aggr.
2. Changed certain parameters in the cell calling algorithm for improved results. Changed the Empty Drops stage for multispecies experiments to call cells using only the UMI counts for each species separately.
3. Raw feature barcode matrices are no longer output by cellranger aggr. It is no longer possible to specify --force-cells of an aggr output in cellranger reanalyze with more cells than were originally called.
4. The secondary analysis implementation is now shared with Loupe Browser's Filtering and Reclustering Wizard. These changes improve the performance of most stages, in either time (t-SNE) or memory (PCA). There will be changes in outputs compared to previous versions, reflecting either slight variations in outputs (PCA, t-SNE), or as if a different randomized seed had been chosen (graph clustering, UMAP).
5. Starting from Cell Ranger 6.1, antibody histograms of UMI counts are shown on Library tab of the web_summary.html, and protein aggregate barcodes are provided as aggregate_barcodes.csv. These are meant to help feature barcoding users diagnose issues of aggregating antibodies on cell surface proteins.
6. Fixed a bug that caused Cell Ranger v4 and higher to ignore user-supplied parameters to --nthreads, defaulting to 1. Parallelization has been re-enabled in Cell Ranger 6.1.

## Deprecating OS

1. The recommended operating systems for Cell Ranger 6.1 are CentOS 7 or Ubuntu 14 Linux variants or newer. CentOS 6 and Ubuntu 12 are still supported but have been deprecated. Support may be dropped in future versions. See the OS support page for more details.

# Release notes for Cell Ranger 6.0.2 (June 16, 2021):

## Bug Fixes

1. Fixes additional issues with file copying on BeeGFS filesystems.
2. cellranger multi: Adds an optional min-assignment-confidence in the config CSV to allow adjustment of the Cell Multiplexing minimum assignment confidence threshold (default: 0.9). Decreasing the threshold will likely increase the number of singlets assigned to samples, but at the cost of potentially increasing the rate of mis-assignment.
3. Adds a warning to the cellranger multi web_summary.html if contaminant tags are detected in Cell Multiplexing experiments.

# Release notes for Cell Ranger 6.0.1 (April 13, 2021):

## General Improvements

1. The fetch-imgt script, to build an IMGT-compatible custom reference for Single Cell Immune Profiling data analysis, has been updated to be compatible with Python 3.
2. Cell Multiplexing analysis has been updated to be more memory-efficient.

## Bug Fixes

1. The [sample] section of the configuration CSV file is now required for Cell Multiplexing analysis.
2. Fixes an issue where cellranger multi would only accept a single VDJ library.
3. Fixes an issue where cellranger vdj preflight would fail if custom primers were passed in.
4. The sample_id information in Cell Ranger 6 aggr runs are now correctly propagated to Loupe Browser.
5. Fixes an issue where .vloupe files fail to generate on some filesystems and operating systems.
6. Fixes an issue in cluster mode where the pipeline could fail to correctly identify which jobs were still queued.
7. Fixes an issue where including an aggr csv in reanalyze would cause the pipeline to exit.
8. The "Number of reads for Custom Feature by Physical library ID" in the multi web summary and metrics summary is now rendered properly.
9. Fixes an issue with file copying on BeeGFS filesystems.

# Release notes for Cell Ranger 6.0.0 (March 2, 2021):

## New Feature: Cell Multiplexing

1. Cell Ranger 6.0 now supports analysis of Cell Multiplexing data for the 3' Gene Expression, Targeted Gene Expression, and Feature Barcode solutions. Instructions for running the cellranger multi subcommand are described in the running multi page. A new Getting Started Tutorial is also available. The Cell Multiplexing algorithms include a new method to call singlets, multiplets, and empty drops. The output file structure has also changed to accomodate multiple samples multiplexed in a single GEM well.

2. The aggr subcommand now supports analysis of cellranger multi outputs for the 3' Gene Expression, Targeted Gene Expression, and Feature Barcode solutions. Further details are described in the running aggr page.

## New Feature: LT (Low Throughput) support

1. Cell Ranger 6.0 supports the analysis of data from 3' Gene Expression and Feature Barcode (Cell Surface Protein) LT (Low Throughput) kits.

## Changes that apply to Gene Expression and Feature Barcode analysis

1. The column names for the Aggregation CSV file required by the aggr sub-command have changed: library_id has been changed to sample_id and library_outs has been changed to sample_outs. Further details are described in the running aggr page.

2. The molecule_info.h5 and unfiltered feature-barcode matrix files (raw_feature_bc_matrix in H5 and MEX formats) will only contain barcodes with at least one read, rather than all barcodes in the whitelist.

3. The change to the unfiltered feature-barcode matrix summarized in (4) above results in a subtle change to the distribution of UMI counts amongst background, i.e. non-cell barcodes, which results in minor changes to the results of the cell calling algorithm. This change occurs due to the second step that identifies non-ambient cell-barcodes as described in the algorithms page.

4. Cell Ranger 6.0 is the first Cell Ranger release to use Python 3.

## Bug fixes and deprecations

1. A bug has been fixed in the graph-based clustering output: previously, in a sample with K clusters, the first K cell-associated barcodes (ordered as in the filtered feature-barcode matrix) may have been assigned incorrect cluster labels. This change does not affect the number of clusters output.

2. A bug has been fixed for multi-genome experiments, wherein the species annotation may have been incorrect for cell-associated barcodes identified by the second step of the cell-calling algorithm, as described in the algorithms page. Changes in metrics are expected to be minor, unless the the proportion of such cells is large.

3. The --qc option has been deprecated from cellranger mkfastq.

4. A bug has been fixed for multi-genome experiments, wherein the species annotation may have been incorrect for cell-associated barcodes identified by the second step of the cell-calling algorithm, as described in the algorithms page. Changes in metrics are expected to be minor, unless the the proportion of such cells is large.

## Changes that apply to 5' Immune Profiling analysis

In Cell Ranger 6.0, the following changes apply to joint analysis of Immune Profiling, Gene Expression, and Feature Barcode data with the multi sub-command:

1. The structure of the outs folder has been updated, as described in running cellranger multi.

2. When running the cellranger aggr subcommand on samples that have immune profiling, gene expression, and/or feature barcode data analyzed with multi, the sample_outs field now contains the path to the outputs for that sample (e.g. outs/per_sample_outs/sample_x). Further details are described in running aggr.

Cell Ranger 6.0 also introduces some improvements and bug fixes related to the clonotype inference algorithms:

1. There are subtle changes to clonotyping heuristics that have little effect on overall behavior, but recover a small number of joins that were previously missed and might be critical for a particular experiment. These changes are described in terms of technical parameters to the algorithm, specifically raising the default for MAX_DIFFS from 50 to 55 and raising the default for MAX_CDR3_DIFFS from 10 to 15. There were also compensatory changes to prevent the rate of false positive joins from increasing: the default for MAX_DEGRADATION was lowered from 3 to 2, and the default for MAX_SCORE was lowered from 1,000,000 to 500,000. For more details, visit enclone help.

2. Single-chain clonotypes are now more likely to be merged with two-chain and three-chain clonotypes. This causes significantly more clonotypes to have single-chain exact subclonotypes.

3. Fixed a bug that caused failures on some very short (defective) V gene reference sequences.

4. The algorithm for deciding to use a donor reference allele now checks all donor reference alleles for all V genes having the same name as the one originally assigned to a contig. For more details, visit enclone help.

5. A doublet test has been added. This removes some exact subclonotypes that appear to represent doublets. Details are documented on the Enclone pages. The typical effect is to remove some three-chain and four-chain clonotypes, with the fraction removed depending on the emperical doublet rate. In some cases, large, complex clonotypes are accurately split into multiple smaller clonotypes by this change.

6. There is no longer a restriction on the length of CDR3 sequences (previously maximum 27).

7. The immune profiling output file all_contig_annotations.csv contains new fields fwr1, ..., fwr4 and cdr1, cdr2, providing the amino acid sequences of framework and complementarity-determining regions (in addition to cdr3, which was already present). The definitions used to define these regions are provided in the enclone features page. The corresponding nucleotide sequences are provided (e.g.fwr1_nt). These fields are also provided in the file consensus_annotations.csv, as are nucleotide start and end positions (e.g. fwr1_start).

8. The immune profiling output file all_contig_annotations.csv contains new field exact_subclonotype_id providing the exact subclonotype ID to which the cell barcode was assigned. Details about exact subclonotypes can be found on the clonotype grouping page.

9. The --qc option has been deprecated from cellranger mkfastq.