@ARTICLE{Krieg2018-zs,
  title    = "High-dimensional single-cell analysis predicts response to
              {anti-PD-1} immunotherapy",
  author   = "Krieg, Carsten and Nowicka, Malgorzata and Guglietta, Silvia and
              Schindler, Sabrina and Hartmann, Felix J and Weber, Lukas M and
              Dummer, Reinhard and Robinson, Mark D and Levesque, Mitchell P
              and Becher, Burkhard",
  abstract = "Immune-checkpoint blockade has revolutionized cancer therapy. In
              particular, inhibition of programmed cell death protein 1 (PD-1)
              has been found to be effective for the treatment of metastatic
              melanoma and other cancers. Despite a dramatic increase in
              progression-free survival, a large proportion of patients do not
              show durable responses. Therefore, predictive biomarkers of a
              clinical response are urgently needed. Here we used
              high-dimensional single-cell mass cytometry and a bioinformatics
              pipeline for the in-depth characterization of the immune cell
              subsets in the peripheral blood of patients with stage IV
              melanoma before and after 12 weeks of anti-PD-1 immunotherapy.
              During therapy, we observed a clear response to immunotherapy in
              the T cell compartment. However, before commencing therapy, a
              strong predictor of progression-free and overall survival in
              response to anti-PD-1 immunotherapy was the frequency of
              CD14+CD16-HLA-DRhi monocytes. We confirmed this by conventional
              flow cytometry in an independent, blinded validation cohort, and
              we propose that the frequency of monocytes in PBMCs may serve in
              clinical decision support.",
  journal  = "Nat. Med.",
  volume   =  24,
  number   =  2,
  pages    = "144--153",
  month    =  feb,
  year     =  2018,
  language = "en"
}

@ARTICLE{Weber2019-qp,
  title    = "{HDCytoData}: Collection of high-dimensional cytometry benchmark
              datasets in Bioconductor object formats",
  author   = "Weber, Lukas M and Soneson, Charlotte",
  abstract = "Benchmarking is a crucial step during computational analysis and
              method development. Recently, a number of new methods have been
              developed for analyzing high-dimensional cytometry data. However,
              it can be difficult for analysts and developers to find and
              access well-characterized benchmark datasets. Here, we present
              HDCytoData, a Bioconductor package providing streamlined access
              to several publicly available high-dimensional cytometry
              benchmark datasets. The package is designed to be extensible,
              allowing new datasets to be contributed by ourselves or other
              researchers in the future. Currently, the package includes a set
              of experimental and semi-simulated datasets, which have been used
              in our previous work to evaluate methods for clustering and
              differential analyses. Datasets are formatted into standard
              SummarizedExperiment and flowSet Bioconductor object formats,
              which include complete metadata within the objects. Access is
              provided through Bioconductor's ExperimentHub interface. The
              package is freely available from
              http://bioconductor.org/packages/HDCytoData.",
  journal  = "F1000Res.",
  volume   =  8,
  pages    = "1459",
  month    =  aug,
  year     =  2019,
  keywords = "Bioconductor; ExperimentHub; benchmarking; clustering;
              differential analyses; high-dimensional cytometry",
  language = "en"
}

@article{Hauchamps2025-zm,
author = {Hauchamps, Philippe and Delandre, Simon and Temmerman, Stéphane T. and Lin, Dan and Gatto, Laurent},
title = {Visual Quality Control With CytoMDS, a Bioconductor Package for Low Dimensional Representation of Cytometry Sample Distances},
journal = {Cytometry Part A},
volume = {107},
number = {3},
pages = {177-186},
doi = {https://doi.org/10.1002/cyto.a.24921},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cyto.a.24921},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cyto.a.24921},
abstract = {ABSTRACT Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, the identification of potential batch effects and outlying samples is paramount to avoid mistaking these effects for true biological signals in downstream analyses. However, this task can prove to be delicate and tedious, especially for datasets with dozens of samples. Here, we present CytoMDS, a Bioconductor package implementing a dedicated method for low-dimensional representation of cytometry samples composed of marker expressions for up to millions of single cells. This method allows a global representation of all samples of a study, with one single point per sample, in such a way that projected distances can be visually interpreted. CytoMDS uses Earth Mover's Distance for assessing dissimilarities between multi-dimensional distributions of marker expression and Multi-Dimensional Scaling for low-dimensional projection of distances. Some additional visualization tools, both for projection quality diagnosis and for user interpretation of the projection coordinates, are also provided in the package. We demonstrate the strengths and advantages of CytoMDS for QC of cytometry data on three real biological datasets, revealing the presence of low-quality samples, batch effects, and biological signal between sample groups.},
year = {2025}
}




@ARTICLE{Stamm2014-wp,
  title     = "AlignMe - a membrane protein sequence alignment web server",
  author    = "Stamm, Marcus and Staritzbichler, René and Khafizov, Kamil and
               Forrest, Lucy R",
  journal   = "Nucleic Acids Res.",
  publisher = "Oxford University Press (OUP)",
  volume    =  42,
  number    = "Web Server issue",
  pages     = "W246--51",
  abstract  = "We present a web server for pair-wise alignment of membrane
               protein sequences, using the program AlignMe. The server makes
               available two operational modes of AlignMe: (i) sequence to
               sequence alignment, taking two sequences in fasta format as
               input, combining information about each sequence from multiple
               sources and producing a pair-wise alignment (PW mode); and (ii)
               alignment of two multiple sequence alignments to create
               family-averaged hydropathy profile alignments (HP mode). For the
               PW sequence alignment mode, four different optimized parameter
               sets are provided, each suited to pairs of sequences with a
               specific similarity level. These settings utilize different types
               of inputs: (position-specific) substitution matrices, secondary
               structure predictions and transmembrane propensities from
               transmembrane predictions or hydrophobicity scales. In the second
               (HP) mode, each input multiple sequence alignment is converted
               into a hydrophobicity profile averaged over the provided set of
               sequence homologs; the two profiles are then aligned. The HP mode
               enables qualitative comparison of transmembrane topologies (and
               therefore potentially of 3D folds) of two membrane proteins,
               which can be useful if the proteins have low sequence similarity.
               In summary, the AlignMe web server provides user-friendly access
               to a set of tools for analysis and comparison of membrane protein
               sequences. Access is available at
               http://www.bioinfo.mpg.de/AlignMe.",
  month     =  jul,
  year      =  2014,
  language  = "en"
}

@ARTICLE{Stamm2013-kz,
  title     = "Alignment of helical membrane protein sequences using AlignMe",
  author    = "Stamm, Marcus and Staritzbichler, René and Khafizov, Kamil and
               Forrest, Lucy R.",
  journal   = "PLoS One",
  publisher = "Public Library of Science (PLoS)",
  volume    =  8,
  number    =  3,
  pages     = "e57731",
  abstract  = "Few sequence alignment methods have been designed specifically
               for integral membrane proteins, even though these important
               proteins have distinct evolutionary and structural properties
               that might affect their alignments. Existing approaches typically
               consider membrane-related information either by using
               membrane-specific substitution matrices or by assigning distinct
               penalties for gap creation in transmembrane and non-transmembrane
               regions. Here, we ask whether favoring matching of predicted
               transmembrane segments within a standard dynamic programming
               algorithm can improve the accuracy of pairwise membrane protein
               sequence alignments. We tested various strategies using a
               specifically designed program called AlignMe. An updated set of
               homologous membrane protein structures, called HOMEP2, was used
               as a reference for optimizing the gap penalties. The best of the
               membrane-protein optimized approaches were then tested on an
               independent reference set of membrane protein sequence alignments
               from the BAliBASE collection. When secondary structure (S)
               matching was combined with evolutionary information (using a
               position-specific substitution matrix (P)), in an approach we
               called AlignMePS, the resultant pairwise alignments were
               typically among the most accurate over a broad range of sequence
               similarities when compared to available methods. Matching
               transmembrane predictions (T), in addition to evolutionary
               information, and secondary-structure predictions, in an approach
               called AlignMePST, generally reduces the accuracy of the
               alignments of closely-related proteins in the BAliBASE set
               relative to AlignMePS, but may be useful in cases of extremely
               distantly related proteins for which sequence information is less
               informative. The open source AlignMe code is available at
               https://sourceforge.net/projects/alignme/, and at
               http://www.forrestlab.org, along with an online server and the
               HOMEP2 data set.",
  month     =  mar,
  year      =  2013,
  language  = "en"
}


@ARTICLE{Hessa2007-rc,
  title     = "Molecular code for transmembrane-helix recognition by the {Sec61}
               translocon",
  author    = "Hessa, Tara and Meindl-Beinker, Nadja M and Bernsel, Andreas and
               Kim, Hyun and Sato, Yoko and Lerch-Bader, Mirjam and Nilsson,
               Ingmarie and White, Stephen H and von Heijne, Gunnar",
  journal   = "Nature",
  publisher = "Springer Science and Business Media LLC",
  volume    =  450,
  number    =  7172,
  pages     = "1026--1030",
  abstract  = "Transmembrane alpha-helices in integral membrane proteins are
               recognized co-translationally and inserted into the membrane of
               the endoplasmic reticulum by the Sec61 translocon. A full
               quantitative description of this phenomenon, linking amino acid
               sequence to membrane insertion efficiency, is still lacking.
               Here, using in vitro translation of a model protein in the
               presence of dog pancreas rough microsomes to analyse a large
               number of systematically designed hydrophobic segments, we
               present a quantitative analysis of the position-dependent
               contribution of all 20 amino acids to membrane insertion
               efficiency, as well as of the effects of transmembrane segment
               length and flanking amino acids. The emerging picture of
               translocon-mediated transmembrane helix assembly is simple, with
               the critical sequence characteristics mirroring the physical
               properties of the lipid bilayer.",
  month     =  dec,
  year      =  2007,
  language  = "en"
}

