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Change Log

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[1.4.4] - 10.12.2020

Changed

  • Hotfix linked to the issues like #125 showing that python3.8 and above crash on modified loop.
  • Fixed bug that checks filenames length instead of number of files.

[1.4.3] - 05.10.2019

Added

  • Support for multiple files as input. This allows you to not merge different lanes before running CITE-seq-Count. Thanks to @arkal for the contribution on this! #79
  • UMI correction for UMI clusters of 1 UMI is now skipped, increases performance.

Changed

  • Cell barcode whitelist is now based on reads instead of UMIs. Hopefully fixing issues #35,#37,#48,#49,#69
  • Added a low filter for UMI correction not trying to correct unique UMIs. Small improvement on performance.
  • Corrected typo from --no_umi_correctio to --no_umi_correction. Thanks for the catch @ChristophH. #72

[1.4.2] - 11.03.2019

Added

  • Cell barcode correction based on whitelist instead of top cells. This will trigger automatically when a whitelist is provided.
  • UMI correction for tags with more than than 20'000 umis is too slow right now. Cells containing a TAG with more than 20'000 umis will be discarded. This means that more than 20'000 antibody tags with different umis have been captured which is odd. Such high numbers could mean that cells have been aggregating and can't be used for downstream analysis. The number of cells are reported under Bad cells and the dense matrix of those cells is provided in uncorrected_cells. Fixing issues #32
  • The option to not correct for umis with --no_umi_correction.
  • Now prints how many reads will be processed.
  • Checks that tags are indeed made of ATGC bases.
  • Added a check for cell barcode correction and collapsing threshold for given whitelist.
  • New option to allow for a sliding window when aligning TAGS. --sliding-window Use when you have a variable sequence before your tags.

Changed

  • Sparse matrix is now based on int32 instead of int16. Fixing issue #40
  • Cell barcode correction without a whitelist is not outputing any error anymore. This is due to the fact that it uses a hard threshold based on the -cells option if it kind find one. Fixing issues #29, #36
  • Upgraded umi_tools to 1.0.0
  • fixed the error linked to umi_tools version update getCellWhitelist #45.

[1.4.0] - 24.01.2019

Added

  • Enabled parallelization using multiprocessing. You can choose how many cores/threads you want to use with the -T --threads option. Takes max cpu by default. It will run on only one core if you run 1'000'000 reads or less.
  • The output is now given in a gzipped mtx format. This is to ensure smooth usage for new/larger datasets such as data from novaseq sequencers. For those who still want to use the dense format, there is an option --dense which will add the dense output as a tsv format. The sparse outputs are compatible the Read10x from the Seurat package.
  • You get both umi and read counts as output. This can help with overamplification estimation.
  • UMI and cell barcodes are now corrected based on umi_tools.
  • A small report is now produced as report.yaml giving some statistics about the run as well as which parameters have been used.
  • Unittesting has been added using pytest. This should help prevent further unforseen bugs.
  • New option for trimming the start of read2 sequences. --start-trim
  • --version option now prints the version.

Changed

  • CITE-seq-Count uses less memory and is faster.
  • Changed how you select the unmapped tags. The option is now -ut instead of -uc and gives you the top most unmapped tags.
  • Expected cells, -cells is now required and not mutually exclusive with whitelists.
  • Hamming distance option has been changed from -hd to --max-error. Please refer to the documentation for more information.

Removed

  • The default dense output matrix is gone and replaced by the sparse equivalent. See the documentation on how to read it.
  • Regex is gone. Finding out what doesn't map is given by the unmapped file option.

[1.3.4]

Added

  • Stripping of numbers and dash (-) to the whitelist barcodes so the 10X barcodes.tsv file could be directly used.
  • When TAGs are too close based on the maximum Levenshtein distance allowed, print the offending pairs with its distance to identify them.
  • When storing unmatched tags, added the possibility to specify a minimum counts cutoff to avoid printing every unmatched possibility (option -uc).
  • Documentation to the functions.

Changed

  • Levenshtein.hamming usage was replaced by Levenshtein.distance in order to keep the sequences with INDELs matching against the TAGs.
  • The redundant classifications no_match and bad_struct were merged into one (no_match); now, sequences match or do not.
  • Decreased the running time by compiling the regex.
  • File handles are released once done reading Read1/Read2 files.
  • The different length TAGs are now being matched: first, by the regex pattern, which will always pick the longest match within the maximum distance allowed; second, by looping through the TAGs sorted by decreasing length, and choosing the first match within the maximum distance allowed.
  • General code improvements and optimisations: releasing file handles sooner, compartimentalising code, etc.

Removed

  • Removed ambiguous classification; it's not a possibility because it's being handled when checking the distance between TAGs. E.g. Lets assume max_hamming_distance = 2, then: if any two TAGs are 2 hamming distance away from each other, then the program aborts with a message; thus, we could never be finding two sequences being two hamming distance away from more than one TAG.

Fixed

  • Reduced the memory usage by 1/3 by removing the UMI_reduce set; because it was based on the unique_lines set, where Read1 is already trimmed to Barcode+UMI length, it was providing no gain. (Issue #17).
  • Hamming distance was not being properly applied because the equal sign was missing from the the conditional (if min_value >= args.hamming_thresh).
  • When applying the regular expression to filter patterns to keep, by using the i in the fuzzy logic it was only accepting insertions as mismatches, not allowing proper mismatches or deletions. The i was changed to s, which means substitution in general.
  • The merge technique for creating the common regex pattern for all the requested ABs/TAGs was replaced by a simple in list technique; the merge technique was faulty, matching sequences that are clearly different (bad_struct), being later classified as no_match instead. For example: Tag1 AAAAAA Tag2 TTTTTT Pattern [AT][AT][AT][AT][AT][AT] Read2 ATATAT... In this scenario, Read2 is being matched and classified as no_match instead of bad_struct.

[1.3.2] - 2018-10-22

Added

  • Printing version now from the help
  • Added a possibility to store unmatched tags in a file using the option -u

[1.3] - 2018-09-05

Added

  • -l --legacy option now available. This option will then end of the regex to find the TAG barcode in R2. Use -l if you have TAG barcodes ending with [TGC] + polyA tails.
  • New warning that checks R1 length in the fastq file and the cell and umi barcodes given as input.

Changed

  • TAG barcode sequence are now added to the name of the tag in the rows of the results. This will help reproducibility since the barcode sequence will already be in the count results.

[1.2] - 2018-08-02

Added

  • pandas dependcy

[1.1] - 2018-08-02

Changed

  • Cite-seq-Count is now a python package!

[0.2] - 2018-07-17

Added

  • Compatibility with tags of different lengths
  • Possibility to use a whitelist of barcodes to extract
  • Uses now fuzzy regex for structure detection

Changed

  • Regex is now optional.
  • You can now use only one tag, the script won't crash

[0.1] - 2017-08-07

Added

  • Processing of CITE-seq data with antibody tags and/or HTO