{% extends "foods/base.html" %} {% load i18n %} {% block title %}{% trans "Foods-Dish-Edit" %}{% endblock %} {% block content %}
{% trans "Below is a form in which you can edit the dish." %}
{% trans "Required Field *" %}


Programmer / Web Developer, @processwire CMS/CMF & @jekyll enthusiast.
Content belongs to author. Design concept is free for study, use, reproduce and distribute.


Tempo-Spatial Map: UNHCR Population Statistics
Define custom filter.
1) To select a country please click on the one you prefer to get information about or use the drop down list and choose the country you like
2) By sliding over the countries you will get the country name as information
3) In the first diagram in the manual side bar, you get more information about refugees over time of the selected country
4) Use the mousewheel to zoom or click on the zoom button in the manual side bar
5) You can change the migration between - refugees who have left their origin - and the ones who have fled into the country you selected
6) To filter the mass of information and connections between two countries, please use the drop down list and choose the type of migration
7) The time slider shows changes of the number of refugees based on the selected country
8) If you like to get more information about the changes of migration between two time steps click twice on the time slider and choose what you like to compare.


Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
| Version: | 2.0.0 |
| Depends: | R (≥ 3.0.2), partykit, Formula, rpart, stats |
| Published: | 2018-09-28 |
| Author: | Elise Dusseldorp [aut, cre, cph], Lisa Doove [aut], Jeanne van de Put [aut], Cor Ninaber [ctb] (supported with the plot function), Iven Van Mechelen [aut, cph], Juan Claramunt Gonzalez [ctb] |
| Maintainer: | Elise Dusseldorp <elise.dusseldorp at fsw.leidenuniv.nl> |
| License: | GPL-2 |
| URL: | http://www.elisedusseldorp.nl/pdf/Dusseldorpetal2015BRM_QUINT.pdf |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | quint results |
| Reference manual: | quint.pdf |
| Package source: | quint_2.0.0.tar.gz |
| Windows binaries: | r-devel: quint_2.0.0.zip, r-release: quint_2.0.0.zip, r-oldrel: quint_2.0.0.zip |
| OS X binaries: | r-release: quint_2.0.0.tgz, r-oldrel: quint_2.0.0.tgz |
| Old sources: | quint archive |
Please use the canonical form https://CRAN.R-project.org/package=quint to link to this page.


This intention replaces initialization of field via assignment with field initializer.
The code is highlighted only if field initializer is located in the initializer and joining with the field declaration is likely to be safe.
In other cases the inspection works like intention action as it may change the code semantics.
Since 2017.2


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Introduction
The system is designed to evaluate a new recommendation algorithm as compared to a text based algorithm. For every time you search,
you will be shown 5 recommendations from both algorithms, randomly shuffled (not ranked). Please rate the recommendations based on
how much they helped you complete your task. Note that they need not give you exact solutions, they can give high level ideas or hints.
Please rate at least 15 recommendations per problem to submit and proceed to the next problem. Do not use search engines as that will impact
the study.
Example
Try solving the problem in the
test tab in the navigation bar.
While you are at it, explore the voting interface and observe the changes in recommendations based on feature weights. As an example, see below screenshots
- Type in the method outline that you are looking for and hit 'search'. You will see some recommendations come up
- From the recommendations on the right, you found one to be somewhat related to conversion between values so gave it 2 stars. As soon as you voted,
notice your updated vote count out of 15. Once this crosses 15, submit button will be enabled.
- With the hope of getting some related methods, you start writing comments and increase your 'Language' feature weights because you want to focus on
matching comments. This helps you get a couple more relevant recommendations to vote for.
1. Type in comments
2. Increase weight for Language features
3. Recommendation involving 'convertion'
4. Recommendation involving 'integer'.
- Integer.toString() looks like it might do the job but it returns a String while you need to return an int. So you change focus to 'Structure' and 'Concept'
features more and reduce weight on 'Language'. This helps you find Integer.parseInt(), which seems more like what you are looking for..
1. Increase Structure (syntactical matches)
2. Increase Concepts (conceptual matches)
Reduce Language back
Problem 1 and 2
Note that sometimes you do not find exactly what you are looking for. In such cases, you should try writing something on your own and search after a couple of
lines so that the algorithm has more data to recommend on. Remember the objective is to vote for recommendations that are relevant and helpful samples, not exact.
Also note that you have to write only ONE method per problem. Do not split your algorithm into two methods.
Start solving the two attemps HERE after reading the FAQ page thoroughly.
Feedback
Please spend a couple of minutes to give us anonymous feedback
here.


Assuming that foo is a valid list, the expression fp.isEq( fp.hd(foo), fp.hd( fp.tl(foo) ) ) ...
Review the API for the three functions involved.
Consider all possible relevant cases for the value of foo.


Welcome to the Bus Mall market survey. Bus Mall is dedicated to providing its captive audience with the best products out there... not to mention the best value-based pricing! By taking the following survey, you allow us to optimize both of these parameters.
Take a look at the images below. Click on the one you find most appealing. The images will then reload. When they do, pick your favorite out of that set, too. We ask that you pick your favorite image out of the three displayed images, for a total of 25 times. Once you have completed this task, you can click the button "See Results" to see a chart showing the total clicks for each BusMall product, followed by another chart showing the ratio of clicks per view for each BusMall product. We thank you for your help. You can click the button "Run Survey Again" to start the survey over.


Abstract
In this short tutorial, we will be going over the distributed package of PyTorch. We'll see how to set up the distributed setting, use the different communication strategies, and go over some the internals of the package.
The distributed package included in PyTorch (i.e., torch.distributed
) enables researchers and practitioners to easily parallelize their computations across processes and clusters of machines. To do so, it leverages the messaging passing semantics allowing each process to communicate data to any of the other processes. As opposed to the multiprocessing (torch.multiprocessing
) package, processes can use different communication backends and are not restricted to being executed on the same machine.
In order to get started we need the ability to run multiple processes simultaneously. If you have access to compute cluster you should check with your local sysadmin or use your favorite coordination tool. (e.g., pdsh, clustershell, or others) For the purpose of this tutorial, we will use a single machine and fork multiple processes using the following template.
"""run.py:"""
#!/usr/bin/env python
import os
import torch
import torch.distributed as dist
from torch.multiprocessing import Process
def run(rank, size):
""" Distributed function to be implemented later. """
pass
def init_processes(rank, size, fn, backend='tcp'):
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = '<IP_ADDRESS>'
os.environ['MASTER_PORT'] = '29500'
dist.init_process_group(backend, rank=rank, world_size=size)
fn(rank, size)
if __name__ == "__main__":
size = 2
processes = []
for rank in range(size):
p = Process(target=init_processes, args=(rank, size, run))
p.start()
processes.append(p)
for p in processes:
p.join()
The above script spawns two processes who will each setup the distributed environment, initialize the process group (dist.init_process_group
), and finally execute the given run
function.
Let's have a look at the init_processes
function. It ensures that every process will be able to coordinate through a master, using the same ip address and port. Note that we used the TCP backend, but we could have used MPI or Gloo instead. (c.f. Section 5.1) We will go over the magic happening in dist.init_process_group
at the end of this tutorial, but it essentially allows processes to communicate with each other by sharing their locations.
|
Send and Recv |
A transfer of data from one process to another is called a point-to-point communication. These are achieved through the send
and recv
functions or their immediate counter-parts, isend
and irecv
.
"""Blocking point-to-point communication."""
def run(rank, size):
tensor = torch.zeros(1)
if rank == 0:
tensor += 1
# Send the tensor to process 1
dist.send(tensor=tensor, dst=1)
else:
# Receive tensor from process 0
dist.recv(tensor=tensor, src=0)
print('Rank ', rank, ' has data ', tensor[0])
In the above example, both processes start with a zero tensor, then process 0 increments the tensor and sends it to process 1 so that they both end up with 1.0. Notice that process 1 needs to allocate memory in order to store the data it will receive.
Also notice that send
/recv
are blocking: both processes stop until the communication is completed. On the other hand immediates are non-blocking; the script continues its execution and the methods return a DistributedRequest
object upon which we can choose to wait()
.
"""Non-blocking point-to-point communication."""
def run(rank, size):
tensor = torch.zeros(1)
req = None
if rank == 0:
tensor += 1
# Send the tensor to process 1
req = dist.isend(tensor=tensor, dst=1)
print('Rank 0 started sending')
else:
# Receive tensor from process 0
req = dist.irecv(tensor=tensor, src=0)
print('Rank 1 started receiving')
print('Rank 1 has data ', tensor[0])
req.wait()
print('Rank ', rank, ' has data ', tensor[0])
Running the above function might result in process 1 still having 0.0 while having already started receiving. However, after req.wait()
has been executed we are guaranteed that the communication took place, and that the value stored in tensor[0]
is 1.0.
Point-to-point communication is useful when we want a fine-grained control over the communication of our processes. They can be used to implement fancy algorithms, such as the one used in Baidu's DeepSpeech or Facebook's large-scale experiments.(c.f. Section 4.1)
|
Broadcast |
AllGather |
|
Reduce |
AllReduce |
|
Scatter |
Gather |
As opposed to point-to-point communcation, collectives allow for communication patterns across all processes in a group. A group is a subset of all our processes. To create a group, we can pass a list of ranks to dist.new_group(group)
. By default, collectives are executed on the all processes, also known as the world. For example, in order to obtain the sum of all tensors at all processes, we can use the dist.all_reduce(tensor, op, group)
collective.
""" All-Reduce example."""
def run(rank, size):
""" Simple point-to-point communication. """
group = dist.new_group([0, 1])
tensor = torch.ones(1)
dist.all_reduce(tensor, op=dist.reduce_op.SUM, group=group)
print('Rank ', rank, ' has data ', tensor[0])
Since we want the sum of all tensors in the group, we use dist.reduce_op.SUM
as the reduce operator. Generally speaking, any commutative mathematical operation can be used as an operator. Out-of-the-box, PyTorch comes with 4 such operators, all working at the element-wise level:
dist.reduce_op.SUM
,dist.reduce_op.PRODUCT
,dist.reduce_op.MAX
,dist.reduce_op.MIN
.In addition to dist.all_reduce(tensor, op, group)
, there are a total of 6 collectives currently implemented in PyTorch.
dist.broadcast(tensor, src, group)
: Copies tensor
from src
to all other processes.dist.reduce(tensor, dst, op, group)
: Applies op
to all tensor
and stores the result in dst
.dist.all_reduce(tensor, op, group)
: Same as reduce, but the result is stored in all processes.dist.scatter(tensor, src, scatter_list, group)
: Copies the \(i^{\text{th}}\) tensor scatter_list[i]
to the \(i^{\text{th}}\) process.dist.gather(tensor, dst, gather_list, group)
: Copies tensor
from all processes in dst
.dist.all_gather(tensor_list, tensor, group)
: Copies tensor
from all processes to tensor_list
, on all processes.Note: You can find the example script of this section in this GitHub repository.
Now that we understand how the distributed module works, let us write something useful with it. Our goal will be to replicate the functionality of DistributedDataParallel. Of course, this will be a didactic example and in a real-world situtation you should use the official, well-tested and well-optimized version linked above.
Quite simply we want to implement a distributed version of stochastic gradient descent. Our script will let all processes compute the gradients of their model on their batch of data and then average their gradients. In order to ensure similar convergence results when changing the number of processes, we will first have to partition our dataset. (You could also use tnt.dataset.SplitDataset, instead of the snippet below.)
""" Dataset partitioning helper """
class Partition(object):
def __init__(self, data, index):
self.data = data
self.index = index
def __len__(self):
return len(self.index)
def __getitem__(self, index):
data_idx = self.index[index]
return self.data[data_idx]
class DataPartitioner(object):
def __init__(self, data, sizes=[0.7, 0.2, 0.1], seed=1234):
self.data = data
self.partitions = []
rng = Random()
rng.seed(seed)
data_len = len(data)
indexes = [x for x in range(0, data_len)]
rng.shuffle(indexes)
for frac in sizes:
part_len = int(frac * data_len)
self.partitions.append(indexes[0:part_len])
indexes = indexes[part_len:]
def use(self, partition):
return Partition(self.data, self.partitions[partition])
With the above snippet, we can now simply partition any dataset using the following few lines:
""" Partitioning MNIST """
def partition_dataset():
dataset = datasets.MNIST('./data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
size = dist.get_world_size()
bsz = 128 / float(size)
partition_sizes = [1.0 / size for _ in range(size)]
partition = DataPartitioner(dataset, partition_sizes)
partition = partition.use(dist.get_rank())
train_set = torch.utils.data.DataLoader(partition,
batch_size=bsz,
shuffle=True)
return train_set, bsz
Assuming we have 2 replicas, then each process will have a train_set
of 60000 / 2 = 30000 samples. We also divide the batch size by the number of replicas in order to maintain the overall batch size of 128.
We can now write our usual forward-backward-optimize training code, and add a function call to average the gradients of our models. (The following is largely inspired from the official PyTorch MNIST example.)
""" Distributed Synchronous SGD Example """
def run(rank, size):
torch.manual_seed(1234)
train_set, bsz = partition_dataset()
model = Net()
optimizer = optim.SGD(model.parameters(),
lr=0.01, momentum=0.5)
num_batches = ceil(len(train_set.dataset) / float(bsz))
for epoch in range(10):
epoch_loss = 0.0
for data, target in train_set:
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
epoch_loss += loss.data[0]
loss.backward()
average_gradients(model)
optimizer.step()
print('Rank ', dist.get_rank(), ', epoch ',
epoch, ': ', epoch_loss / num_batches)
It remains to implement the average_gradients(model)
function, which simply takes in a model and averages its gradients across the whole world.
""" Gradient averaging. """
def average_gradients(model):
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM)
param.grad.data /= size
Et voilà ! We successfully implemented distributed synchronous SGD and could train any model on a large computer cluster.
Note: While the last sentence is technically true, there are a lot more tricks required to implement a production-level implementation of synchronous SGD. Again, use what has been tested and optimized.
As an additional challenge, imagine that we wanted to implement DeepSpeech's efficient ring allreduce. This is fairly easily implemented using point-to-point collectives.
""" Implementation of a ring-reduce with addition. """
def allreduce(send, recv):
rank = dist.get_rank()
size = dist.get_world_size()
send_buff = th.zeros(send.size())
recv_buff = th.zeros(send.size())
accum = th.zeros(send.size())
accum[:] = send[:]
left = ((rank - 1) + size) % size
right = (rank + 1) % size
for i in range(size - 1):
if i % 2 == 0:
# Send send_buff
send_req = dist.isend(send_buff, right)
dist.recv(recv_buff, left)
accum[:] += recv[:]
else:
# Send recv_buff
send_req = dist.isend(recv_buff, right)
dist.recv(send_buff, left)
accum[:] += send[:]
send_req.wait()
recv[:] = accum[:]
In the above script, the allreduce(send, recv)
function has a slightly different signature than the ones in PyTorch. It takes a recv
tensor and will store the sum of all send
tensors in it. As an exercise left to the reader, there is still one difference between our version and the one in DeepSpeech: their implementation divide the gradient tensor into chunks, so as to optimially utilize the communication bandwidth. (Hint: toch.chunk)
We are now ready to discover some of the more advanced functionalities of torch.distributed
. Since there is a lot to cover, this section is divided into two subsections:
dist.init_process_group()
.One of the most elegant aspects of torch.distributed
is its ability to abstract and build on top of different backends. As mentioned before, there are currently three backends implemented in PyTorch: TCP, MPI, and Gloo. They each have different specifications and tradeoffs, depending on the desired use-case. A comparative table of supported functions can be found here.
So far we have made extensive usage of the TCP backend. It is quite handy as a development platform, as it is guaranteed to work on most machines and operating systems. It also supports all point-to-point and collective functions on CPU. However, there is no support for GPUs and its communication routines are not as optimized as the MPI one.
The Gloo backend provides an optimized implementation of collective communication procedures, both for CPUs and GPUs. It particularly shines on GPUs as it can perform communication without transferring data to the CPU's memory using GPUDirect. It is also capable of using NCCL to perform fast intra-node communication and implements its own algorithms for inter-node routines.
Since version 0.2.0, the Gloo backend is automatically included with the pre-compiled binaries of PyTorch. As you have surely noticed, our distributed SGD example does not work if you put model
on the GPU. Let's fix it by first replacing backend='gloo'
in init_processes(rank, size, fn, backend='tcp')
. At this point, the script will still run on CPU but uses the Gloo backend behind the scenes. In order to use multiple GPUs, let us also do the following modifications:
init_processes(rank, size, fn, backend='tcp')
\(\rightarrow\) init_processes(rank, size, fn, backend='gloo')
model = Net()
\(\rightarrow\) model = Net().cuda(rank)
data, target = Variable(data), Variable(target)
\(\rightarrow\) data, target = Variable(data.cuda(rank)), Variable(target.cuda(rank))
With the above modifications, our model is now training on two GPUs and you can monitor their utilization with watch nvidia-smi
.
The Message Passing Interface (MPI) is a standardized tool from the field of high-performance computing. It allows to do point-to-point and collective communications and was the main inspiration for the API of torch.distributed
. Several implementations of MPI exist (e.g. Open-MPI, MVAPICH2, Intel MPI) each optimized for different purposes. The advantage of using the MPI backend lies in MPI's wide availability - and high-level of optimization - on large computer clusters. Some recent implementations are also able to take advantage of CUDA IPC and GPU Direct technologies in order to avoid memory copies through the CPU.
Unfortunately, PyTorch's binaries can not include an MPI implementation and we'll have to recompile it by hand. Fortunately, this process is fairly simple given that upon compilation, PyTorch will look by itself for an available MPI implementation. The following steps install the MPI backend, by installing PyTorch from sources.
python setup.py install
yet.conda install -c conda-forge openmpi
python setup.py install
.In order to test our newly installed backend, a few modifications are required.
if __name__ == '__main__':
with init_processes(0, 0, run, backend='mpi')
.mpirun -n 4 python myscript.py
.The reason for these changes is that MPI needs to create its own environment before spawning the processes. MPI will also spawn its own processes and perform the handshake described in Initialization Methods, making the rank
and size
arguments of init_process_group
superfluous. This is actually quite powerful as you can pass additional arguments to mpirun
in order to tailor computational resources for each process. (Things like number of cores per process, hand-assigning machines to specific ranks, and some more) Doing so, you should obtain the same familiar output as with the other communication backends.
To finish this tutorial, let's talk about the very first function we called: dist.init_process_group(backend, init_method)
. In particular, we will go over the different initialization methods which are responsible for the initial coordination step between each process. Those methods allow you to define how this coordination is done. Depending on your hardware setup, one of these methods should be naturally more suitable than the others. In addition to the following sections, you should also have a look at the official documentation.
Before diving into the initialization methods, let's have a quick look at what happens behind init_process_group
from the C/C++ perspective.
name2channel.at()
function. A Channel
class is returned, and will be used to perform the data transmission.THDProcessGroupInit()
is called. This instantiates the channel and adds the address of the master node.master
procedure, while all other ranks will be workers
.We have been using the environment variable initialization method throughout this tutorial. By setting the following four environment variables on all machines, all processes will be able to properly connect to the master, obtain information about the other processes, and finally handshake with them.
MASTER_PORT
: A free port on the machine that will host the process with rank 0.MASTER_ADDR
: IP address of the machine that will host the process with rank 0.WORLD_SIZE
: The total number of processes, so that the master knows how many workers to wait for.RANK
: Rank of each process, so they will know whether it is the master of a worker.The shared filesystem requires all processes to have access to a shared file system, and will coordinate them through a shared file. This means that each process will open the file, write its information, and wait until everybody did so. After what all required information will be readily available to all processes. In order to avoid race conditions, the file system must support locking through fcntl. Note that you can specify ranks manually or let the processes figure it out by themselves. Be defining a unique groupname
per job you can use the same file path for multiple jobs and safely avoid collision.
dist.init_process_group(init_method='file:///mnt/nfs/sharedfile', world_size=4,
group_name='mygroup')
Initializing via TCP can be achieved in two different ways:
In the first case, all workers will be able to connect to the process with rank 0 and follow the procedure described above.
dist.init_process_group(init_method='tcp://<IP_ADDRESS>:23456', rank=args.rank, world_size=4)
In the second case, the multicast address specifies the group of nodes who might potentially be active and the coordination can be handled by allowing each process to have an initial handshake before following the above procedure. In addition TCP multicast initialization also supports a group_name
argument (as with the shared file method) allowing multiple jobs to be scheduled on the same cluster.
dist.init_process_group(init_method='tcp://[ff15:1e18:5d4c:4cf0:d02d:b659:53ba:b0a7]:23456',
world_size=4)
I'd like to thank the PyTorch developers for doing such a good job on their implementation, documentation, and tests. When the code was unclear, I could always count on the docs or the tests to find an answer. In particular, I'd like to thank Soumith Chintala, Adam Paszke, and Natalia Gimelshein for providing insightful comments and answering questions on early drafts.


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Blind border collie and best mate need new home after owner dumped them in woods
A blind border collie is looking for a new home with his best friend who just happens to be his guide dog.
Nine-year-old border collies Bess and Roy were found tied up in the woods in Cumbria and taken to the RSPCA.
The charity believes they were previously working collies on a farm but were abandoned when their owner could no longer take care of them.
The best friends have to be rehomed together because Roy is blind in one eye and seriously impaired in the other, so relies on Bess to be his guide dog.
RSPCA Westmorland branch manager Christine Lowe said: ‘They are both lovely, friendly, happy dogs who deserve a home where they’ll get all of the affection they crave as they come into their senior years.’
Advertisement Advertisement
The pair are up for adoption under the branch’s elderly animal rehoming scheme, which means anyone who takes them on will receive ongoing financial assistance with their vet bills.
They have never lived in a domestic environment and are not house-trained, having lived outside for most of their lives.
The pair are both happy being left home alone for short periods of time and know some basic commands, including ‘sit’.
They would be best suited in a home with no cats but would be happy living with a family with older children.
Ms Lowe said: ‘They need a home where they can live out their days in comfort with someone who understands their unique needs.’
To find out more about Bess and Roy, visit their online profile.
Got a story for Metro.co.uk?
Get in touch with our news team by emailing us at<EMAIL_ADDRESS>For more stories like this, check our news page.


Colourette Cosmetics
Chocolat. Chocolat is actually French for Chocolate. So this post is all about the Limited Edition: Chocolat by Colourette.
I recommend Colourette to all beauty enthusiasts and lipstick horders. You will never go wrong with Colourette. Buying it, also shows your support with local make up brands.
With other sophisticated shades to choose from, Colourstick is the perfect definition of love at first swipe.


This a page made to show the different projects done during the semester
Made by Daniel del Castillo de la Rosa


This app takes the provided images and processes them in to abstract vector images. Processing numerous image files may take some time.


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Field
{{ field_bet }}
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exclude: true count: false
name: raw
0.1 Raw data
- Raw count table
## Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6
## ENSG00000000003 321 303 204 492 455 359
## ENSG00000000005 0 0 0 0 0 0
## ENSG00000000419 696 660 472 951 963 689
## ENSG00000000457 59 54 44 109 73 66
## ENSG00000000460 399 405 236 445 454 374
## ENSG00000000938 0 0 0 0 0 1
- Metadata
## Sample_ID Sample_Name Time Replicate Cell
## Sample_1 Sample_1 t0_A t0 A A431
## Sample_2 Sample_2 t0_B t0 B A431
## Sample_3 Sample_3 t0_C t0 C A431
## Sample_4 Sample_4 t2_A t2 A A431
## Sample_5 Sample_5 t2_B t2 B A431
## Sample_6 Sample_6 t2_C t2 C A431
name: norm-1
0.2 Normalisation
.pull-left-50[ - Make counts comparable across samples - Control for sequencing depth
## s1 s2 s1_tc s2_tc
## x 20 6 0.33 0.38
## y 25 6 0.42 0.38
## z 15 4 0.25 0.25
]
–
.pull-right-50[ - Control for sequencing bias
## s1 s2 s1_tc s2_tc
## x 20 20 0.12 0.39
## y 25 25 0.16 0.49
## z 15 4 0.09 0.08
## de 100 2 0.62 0.04
]
name: norm-3
0.3 Normalisation
- CPM, RPKM, FPKM, TPM, RLE, MRN, Q, UQ, TMM, VST, RLOG, VOOM … Too many…
–
- CPM: Controls for sequencing depth when dividing by total count. Not for within-sample comparison or DE.
–
- RPKM/FPKM: Controls for sequencing depth and gene length. Good for technical replicates, not good for sample-sample due to compositional bias. Assumes total RNA output is same in all samples. Not for DE.
–
- TPM: Similar to RPKM/FPKM. Corrects for sequencing depth and gene length. Also comparable between samples but no correction for compositional bias.
–
- TMM/RLE/MRN: Improved assumption: The output between samples for a core set only of genes is similar. Corrects for compositional bias. Used for DE. RLE and MRN are very similar and correlates well with sequencing depth.
edgeR::calcNormFactors()
implements TMM, TMMwzp, RLE & UQ.DESeq2::estimateSizeFactors
implements median ratio method (RLE). Does not correct for gene length.
–
- VST/RLOG/VOOM: Variance is stabilised across the range of mean values. For use in exploratory analyses. Not for DE.
vst()
andrlog()
functions from DESeq2.voom()
function from Limma converts data to normal distribution.
–
- geTMM: Gene length corrected TMM.
.citation[
.cite[ Dillies, Marie-Agnes, et al. “A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.” Briefings in bioinformatics 14.6 (2013): 671-683]
.cite[ Evans, Ciaran, Johanna Hardin, and Daniel M. Stoebel. “Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions.” Briefings in bioinformatics (2017)]
]
name: eda-cor
0.4 Exploratory | Correlation
- Correlation between samples
dmat <- as.matrix(cor(cv,method="spearman"))
pheatmap::pheatmap(dmat,border_color=NA,annotation_col=mr[,"Time",drop=F],
annotation_row=mr[,"Time",drop=F],annotation_legend=T)
name: eda-pca
0.5 Exploratory | PCA
- Relationship between samples
.pull-left-40[ ]
.pull-right-60[ ]
name: dge-sf
0.6 DGE | Size factors
- Normalisation factors are computed
## Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8 Sample_9
## 0.9003753 0.8437393 0.5106445 1.1276451 1.0941383 0.8133849 0.7553903 1.1744008 1.0189325
## Sample_10 Sample_11 Sample_12
## 1.3642797 1.2325485 1.8555904
name: dge-dispersion-2
0.7 DGE | Dispersion
- D is unreliable for low mean counts
- Genes with similar mean values must have similar dispersion
- Estimate likely (ML) dispersion for each gene based on counts
- Fit a curve through the gene-wise estimates
- Shrink dispersion towards the curve
name: dge-test-2
0.8 DGE | Test
##
## out of 14578 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0 (up) : 413, 2.8%
## LFC < 0 (down) : 696, 4.8%
## outliers [1] : 0, 0%
## low counts [2] : 2261, 16%
## (mean count < 26)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
1 Acknowledgements
- Normalising RNA-seq data by Ernest Turro
- RNA-seq analysis Bioconductor vignette
- DGE Workshop by HBC training
title: “Exploratory and differential expression analyses” subtitle: “Workshop on RNA-Seq” author: “Ashfaq Ali | 24-Nov-2019” institute: NBIS, SciLifeLab keywords: bioinformatics, course, scilifelab, nbis output: xaringan::moon_reader: encoding: ‘UTF-8’ self_contained: false chakra: ‘assets/remark-latest.min.js’ css: ‘assets/presentation.css’ lib_dir: libs nature: ratio: ‘4:3’ highlightLanguage: r highlightStyle: github highlightLines: true countIncrementalSlides: false slideNumberFormat: “%current%/%total%” — exclude: true count: false
name: eda-cor ## Exploratory Analyses
- Exploratory analyses on samples
- Detect outliers using PCA
- Filter out low quality samples if possible
- Map meta data on PCA plot to find any confounding factors
Sample to sample correlations
Exploratory analyses on gene level estimates
- Gene counts/library size
- Check if the treatment groups relate to library size
Gene dispersion estimate and visulizations
- Exploratory analyses on statitics
- MA plot of base mean vs. LFC
- Histogram of p-values
Plot counts of individual genes
Look for warning signs and take action on all levels and use visulizations as much as possible.
1.1 Exploratory | Correlation
- Correlation between samples
dmat <- as.matrix(cor(cv,method="spearman"))
pheatmap::pheatmap(dmat,border_color=NA,annotation_col=mr[,"Time",drop=F],
annotation_row=mr[,"Time",drop=F],annotation_legend=T)
name: eda-pca
1.2 Exploratory | PCA
- Relationship between samples
.pull-left-40[ ]
.pull-right-60[ ]
name: dge-sf
1.3 DGE | Size factors
- Normalisation factors are computed
## Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8 Sample_9
## 0.9003753 0.8437393 0.5106445 1.1276451 1.0941383 0.8133849 0.7553903 1.1744008 1.0189325
## Sample_10 Sample_11 Sample_12
## 1.3642797 1.2325485 1.8555904
| .pull-left-50[ ] |
| .pull-right-50[ ] |
name: dge-dispersion
1.4 DGE | Dispersion
- We need a measure variability of gene counts
–
- Dispersion is a measure of variability in gene expression for a given mean
name: dge-test
1.5 DGE | Test
- Log2 fold changes changes are computed after GLM fitting
## [1] "Intercept" "Time_t2_vs_t0" "Time_t24_vs_t0" "Time_t6_vs_t0"
–
- Use
results()
to customise/return results- Set coefficients using
contrast
orname
- Filtering by fold change using
lfcThreshold
cooksCutoff
removes outliersindependentFiltering
pAdjustMethod
alpha
- Set coefficients using
name: dge-test-3
1.6 DGE | Test
## log2 fold change (MLE): Time t2 vs t0
## Wald test p-value: Time t2 vs t0
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSG00000000003<PHONE_NUMBER>30775 0.220619809383817 0.112761083786837 1.95652437857795
## ENSG00000000419<PHONE_NUMBER>99214 0.0592719659603648 0.101481284660981 0.584067950641097
## ENSG00000000457 82.078767893562 0.207748623173336 0.220404896511899 0.942577168026391
## ENSG00000000460<PHONE_NUMBER>0304 -0.129186381676168 0.115139182333228 -1.12200190289945
## ENSG00000001036<PHONE_NUMBER>11436 0.0288826917736049 0.085150069191275 0.339197513847286
## ENSG00000001084<PHONE_NUMBER>01543 0.211964772147083 0.0929810563101642 2.27965545411762
## pvalue padj
## <numeric> <numeric>
## ENSG00000000003 0.0504034135059863 0.263505451677943
## ENSG00000000419 0.55917459631142 0.830262316253793
## ENSG00000000457 0.34589722283033 0.689945926089258
## ENSG00000000460 0.261861630821574 0.612624613827207
## ENSG00000001036 0.734460942031804 0.909638554349496
## ENSG00000001084 0.0226281312354094 0.159263252815164
–
1.6 Use
names: dge-lfcSrink-1lfcShrink()
to correct fold changes for high dispersion genes
1.7 DGE | LFC Shrink
# Perform LFC shrinkage.
dg2<- lfcShrink(dg, res = res1, coef ="Time_t2_vs_t0", quiet = TRUE)
res2 <- dg2[which(dg2$padj<0.05 & abs(dg2$log2FoldChange) >0.58), ]
| .pull-left-50[ |
r summary(res2) |
## ## out of 126 with nonzero total read count ## adjusted p-value < 0.05 ## LFC > 0 (up) : 46, 37% ## LFC < 0 (down) : 80, 63% ## outliers [1] : 0, 0% ## low counts [2] : 0, 0% ## (mean count < 26) ## [1] see 'cooksCutoff' argument of ?results ## [2] see 'independentFiltering' argument of ?results
] |
| .pull-right-50[ |
r res11 <- res1[which(res1$padj<0.05 & abs(res1$log2FoldChange) >0.58), ] # Order based on original LFC summary(res11) |
## ## out of 382 with nonzero total read count ## adjusted p-value < 0.05 ## LFC > 0 (up) : 177, 46% ## LFC < 0 (down) : 205, 54% ## outliers [1] : 0, 0% ## low counts [2] : 0, 0% ## (mean count < 26) ## [1] see 'cooksCutoff' argument of ?results ## [2] see 'independentFiltering' argument of ?results |
| ] |
names: dge-lfcSrink-1
1.8 DGE | LFC Shrink
## log2 fold change (MAP): Time t2 vs t0
##
## DataFrame with 3 rows and 2 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## ENSG00000120875<PHONE_NUMBER>53803 -2.30638870245528
## ENSG00000142627 1572.22058641611 -1.98604476740172
## ENSG00000139289 1300.91800509536 -1.73454657526826
# Order based on original LFC
res11[order(abs(res11$log2FoldChange),decreasing =
TRUE),c(1:2)][1:3,]
## log2 fold change (MLE): Time t2 vs t0
##
## DataFrame with 3 rows and 2 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## ENSG00000120875<PHONE_NUMBER>53803 -2.76414118057782
## ENSG00000142627 1572.22058641611 -2.38780612309795
## ENSG00000139318<PHONE_NUMBER>95382 -2.38675628664027
The ranks of top DE genes changes after lfcSrink
, note the base means.
2 Acknowledgements
- Slides Originally created by Roy Francis at NBIS
- Normalising RNA-seq data by Ernest Turro
- RNA-seq analysis Bioconductor vignette
- DGE Workshop by HBC training
title: “Functional analyses” subtitle: “Workshop on RNA-Seq” author: “Roy Francis | 24-Nov-2019” institute: NBIS, SciLifeLab keywords: bioinformatics, course, scilifelab, nbis output: xaringan::moon_reader: encoding: ‘UTF-8’ self_contained: false chakra: ‘assets/remark-latest.min.js’ css: ‘assets/presentation.css’ lib_dir: libs nature: ratio: ‘4:3’ highlightLanguage: r highlightStyle: github highlightLines: true countIncrementalSlides: false slideNumberFormat: “%current%/%total%” — exclude: true count: false
name: intro
2.1 Introduction
ensembl_gene_id baseMean log2FoldChange pvalue padj
1 ENSG00000000003 490.01721 0.9145204 3.661641e-17 0.00376
2 ENSG00000000419 817.78066 -0.1894651 6.001737e-02 0.04354
3 ENSG00000000457 82.07877 0.3307639 1.207585e-01 0.00005
4 ENSG00000000460 356.07160 -1.8636578 4.096103e-51 0.00025
5 ENSG00000001036 919.60675 -0.3482723 3.922539e-05 0.19231
6 ENSG00000001084 529.59397 -0.6764194 8.192621e-13 0.06244
–
Is there a pattern in my list of DEGs?
–
- Do my DEGs work together?
- Are they involved in a biological process?
- Are they involved in a pathway?
- Reduce gene lists to terms
- Pick interesting genes based on function
- Less prone to false-positives on the gene-level
- Interpretation of genome-wide results
name: go
2.2 Gene sets • GO
.pull-left-50[ - Three categories: Biological process, Molecular function, Cellular component - Displayed as a network graph - Genes are shared between multiple terms
]
.pull-right-50[ - Almost hierarchical - Terms get detailed down the hierarchy - A term can have multiple parents
]
name: gsa
2.3 Gene set analyses (GSA)
- Requires cut-off
- Omits any expression metric
- Good to test overlap of signif genes in two comparisons
- Computationally fast
.size-90[]
name: gsea
2.4 Gene set enrichment analyses (GSEA)
- All genes are used
- Ranked by an expression metric/gene-level statistic
name: tools
2.5 Tools
2.5.1 R packages
topGO, goana, goseq, topKEGG, kegga, enrichR, piano, clusterProfiler, Pathview, fgsea, gprofileR
2.6 Acknowledgements
- Slides by Leif Wigge
name: end_slide class: end-slide, middle count: false
3 Thank you. Questions?
.end-text[
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
OS: macOS Catalina 10.15.1
Built on : 24-Nov-2019 at 20:10:06
2019 • SciLifeLab • NBIS ]


Library loader
This library requires a MapBox token and jQuery, this loader assures that token is available and loads jQuery from cdn if not already loaded in the document. When requirements are satisfied the callback passed to the osmloader#ready method is invoked
Methods
(static) checkToken()
Checks if the MapBox token in set
(static) load()
Loads the library and emits an event when loaded
The event emitted is 'onDrawerLoader'
(static) requirejQuery(then)
Checks if jQuery is loaded, if not loads it, and then execute the cb
Parameters:
| Name | Type | Description |
|---|---|---|
then |
function | callback to execute after jQuery is fully loaded |


Reports any instances of hardcoded strings in your UI forms.
Hardcoded string literals are probably errors in
an internationalized environment.
This inspection won't report empty strings and strings consisting of only whitespace.
There is a quickfix provided that transforms a string literal
into a reference to a property in resource bundle.


{{ user.username }} {{ user.date_joined }}
Do you really want to delete these records? This process cannot be undone.


Our mission is to provide new immigrants with connections to career paths and venture capital funding to help make their transition to America better. We believe that all Americans, no matter where they came from, deserve quick and easy access to the tools and that will help them prosper. We provide excellent services like an automatic resume generator, job searching and connections to investors to help our users be successful in a constantly changing economic environment. We offer our services in 10 languages (and counting!) to make our service accessible to as many as possible. Our goal is to create a better, more prosperous America by helping those who need it most.


{title:'Marshallers'}
{@link oaj.marshaller.Marshaller Marshallers} are simple pairings of a {@link oaj.serializer.Serializer} and {@link oaj.parser.Parser} with convenience methods for serializing and parsing POJOs.
|
|
Juneau comes with the following predefined marshallers:
Each predefined marshaller also includes static convenience from/to methods to make it even easier to perform marshalling on POJOs:
|


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Figure 1
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Structure of 6,7,8,9-tetrahydro-N,N,-di-n-propyl-1 H-benz [g ... |
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Figure 2
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The Ugo-Basile activity cage (type 7401) |
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Figure 3
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Effect of TPBIA on the motor behavior of experimental animal ... |
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Figure 4
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Time course of lipid peroxidation as affected by various con ... |
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Figure 5
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Low energy conformation and van der Waals surface of compoun ... |


04 April 2000
Disease Outbreak Reported
An imported, fatal case of Lassa fever has been reported from Germany. The patient was a Nigerian national, working in Nigeria, who had been transferred to Germany by air ambulance for medical treatment. Clinical samples were sent to the Bernhard-Nocht Institute in Hamburg where the diagnosis of Lassa fever was confirmed by virological tests. Contacts of the patient are being traced, and will be followed up by the public health authorities.


The 2011 Bulwer-Lytton Fiction Contest awards have been announced, and there is some terrific—that is, awful—stuff duly honoured. Go here for the winners and runners-up.
There is one passage that, to my mind, doesn't belong in the list. I would have been proud to write the sentence that made Mike Mayfield the runner-up in the Adventure category. Far from being “bad” writing (deliberately or otherwise), I think this is superb, and rather Hooting Yardy:
Sensing somehow a scudding lay in the offing, Skipper Bob tallied his tasks: reef the mains'l, mizzen, and jib, strike and brail the fores'l, mizzen stays'l and baggywrinkles, bowse the halyards, mainsheets, jacklines and vangs, turtle and belay fast the small cock, flemish the taffrail warps, batten the booby hatch, lay by his sou'wester, and find the bailing bucket.
Long long ago, when I was wet behind the ears, I wrote a story which began: “Oh, I so wanted this to be a seafaring yarn.” Then and now, I could learn a thing or two from Mike Mayfield.
UPDATE : Readers who follow the link to By Aerostat To Hooting Yard and read it for the first time should note that the Dobson in the story is completely unrelated to that well-loved character Dobson the out of print pamphleteer. Just so you know.


jQuery Date/Time Selectors: Martin Milesich's Timepicker
date + time:
alt date:
alt time:
Notes
- Cannot use the time slider with finger, but you can select a point on the slider with finger OK
- Date and time is not registered in the bound field until "done"
- Generally neat extension of the standard datepicker. Supports alternate fields to split out date/time component for easier processing (example above)
References


Welcome to Naama's Portfolio
About Me
I'm a 3rd year Software Engineering student specializing in Data Science.
In high school I realized I enjoy coding and therefore decided to persue a degree in Software Engineering.
Lately I've started an internship at Google. I'm excited to learn new things as well as implement what I've already learned.
Hobbies
On my free time, I enjoy reading books, drawing, cooking or just hanging out with my family or friends.
I also love listening to music (although I can't really play any instrument) and most of the time I have something playing in the background :)
My Family
I have two older sisters, one younger brother and a cat named Pisu (which is 'cat' in Malagasy, Thanks Guy :) ). We also have another cat who found us nice enough to adopt us as her family, her name is Karzi.
You can find pictures of my adorable cats in the gallery. I'm sure you'll like them just as much as I do!
Projects
During my studies I've done a few projects. here are some of the interesting ones.
- Automatic Pet Feeder - this project focused on hardware and different sensors and motors.
The idea was to help disabled pet owners take care of their pets by letting the food bowl fill itself whenever it empties.
- Vacation Booking System - in this project we created a system that lets hosting units owners offer their units to the public and lets people contact them and book a vacation based on their preferences.
- A 3D Scene - in this project we implemented different Software Engineering principles like keeping the code healthy, testing it etc. We rendered a 3D image by creating classes for different geometries, light sources and more.
Here's how I usually spend my day
Here are some of my favorites places in Jerusalem
Find Out More About Me
To find out more about me feel free to visit my LinkedIn page.
Click here to get a random fact about me


PLANET SICK-BOY: http://www.sick-boy.com
Leave it to the Catholic Church to damn something that could actually turn people on to God. They've done it before with other films deemed blasphemous – sight unseen – by their bible-thumping leaders. Remember The Last Temptation of Christ? They were upset because Jesus had a mortal thought. The horror! And then there was Priest, where the leader of a Roman Catholic parish struggled with his homosexuality. Like that's not happening anywhere in the real world.
With Kevin Smith's Dogma, it seems as if the Church has sunk to new lows. While going out of its way to openly mock organized religion, Dogma is probably the biggest pro-God film in decades. It's an effective portrayal of redemption and faith that will really make people think about their current religious beliefs, and it does it in a way that doesn't hit you over the head. Perhaps the Catholic Church is frightened that its flock will see the film and then question the word that has been spoon-fed to them over the years. God forbid people think for themselves.
When Dogma had its US premiere, throngs of vocal protestors yelled at people entering the theater. As Smith (Chasing Amy) hit the red carpet, he tried to explain to them that his movie was a `really devout, pro-faith' film. But as right-wing protest organizer C. Preston Noell III of The American Society for the Defense of Tradition, Family and Property later explained, Smith was wrong. How fortunate for this talented filmmaker to come across someone able to point out to him that a film that he wrote and directed is not what he thinks it is. Noell also stated that the movie is `made by sad, silly people who, in effect, are puppets manipulated by the devil' and that his 200,000 group members planned to picket theaters once the film opened nationally. When asked what he specifically objected to, Noell admitted that he had not seen the film. Understandable, since siphoning money from the wallets of your followers has got to be pretty time-consuming.
So who do you believe? The `puppets manipulated by the devil' or the puppets manipulated by the Catholic Church? See the movie and judge for yourself.  Not with today's ticket prices. If you sneak in without paying, that's another story. And if you're still scared, stay home and watch CBS's prime-time line-up.
Dogma opens (after a hilarious disclaimer discussing film critics and platypuses) on the steps of a Red Bank, New Jersey church, where Cardinal Glick (George Carlin, Bill & Ted's Excellent Adventure) is holding a press conference to announce the launch of the new `Catholicism – WOW' campaign that hopes to plant more asses in church pews. In addition to a new breakfast cereal (the wafer-like Hosties) and the new `Buddy-Christ' (a winking, smiling Jesus giving a thumbs-up), Glick also explains that this particular church is home to an all-forgiving arch that will erase the sins of anyone that passes through it.
News of the arch makes it to Wisconsin, where two fallen angels named Loki (Matt Damon, Rounders) and Bartleby (Ben Affleck, Forces of Nature) have spent their days since being banished from Heaven. Loki and Bartleby used to carry out God's dirty work - back when he was vengeful - and see the arch as one last chance to escape the banality of Wisconsin for the posh confines of Heaven's paradise. The trouble is that if Loki and Bartleby are forgiven, it will prove God's fallibility, which will cause the world to end.
But God is wise to Loki and Bartleby's plans and dispatches angel Metatron (Alan Rickman, Sense and Sensibility) to urge a mortal into a crusade to stop the duo. The mortal is Bethany (Linda Fiorentino, Men in Black), an Illinois Planned Parenthood employee that has recently found herself doubting her Catholic beliefs. Although she initially believes Metatron's visit was just a bad dream, Bethany reluctantly hits the road, meeting - in Wizard of Oz fashion - two horny prophets (Smith's recurring characters Jay and Silent Bob), a forgotten thirteenth Apostle named Rufus (Chris Rock, Lethal Weapon 4) and a stripper/muse named Serendipity (Salma Hayek, Wild Wild West).
Other uniquely cast roles include Bud Cort (Harold & Maude) as a homeless man and best-selling crooner Alanis Morissette as God. If it sounds like too much, it really is – especially Hayek's unnecessary character and the scene with the poop monster. Dogma is unrelentingly and blisteringly hysterical, which, in measured doses, is fantastic. Unfortunately here, like South Park: Bigger, Longer and Uncut, it becomes tiresome after a while, leaving the film with a very uneven feel. But this can easily be overlooked, if not just for being an incredible commercial for the very people that are trying to convince you not to see it. That is perhaps a greater irony than anything in Smith's script.
2:10 - R for strong language including sex-related dialogue, violence, crude humor and some drug content
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IN THE HIGH COURT OF JHARKHAND AT RANCHI A.B.A. No. 1468 of 2018 Rajesh Kumar Singh ...... Petitioner Versus The State of Jharkhand ...... Opposite Party ---------
CORAM: HON'BLE MR. JUSTICE ANANT BIJAY SINGH
---------
For the Petitioner : Mr. Ajay Kr. Sah, Advocate For the State : A.P.P --------- 03/Dated: 08/05/2018
Heard learned counsel for the petitioner and learned counsel for the State.
The petitioner is apprehending his arrest in connection with the case registered under Sections 378 and 379 of the Indian Penal Code, Section 4(21) of JMMC Rules, 1957, Section 54 and 30 of JMMC Rules, 2004, Section 3(a)F of the Jharkhand Rules Supplier and Price Control Act, Section 15 of the Environment Protection Act, 1986 read with Air Protection Control and Pollution Act.
Learned counsel for the petitioner has submitted that petitioner has been falsely implicated in this case. It is also submitted that petitioner is lease holder of Nagpur Sai River of Khata No. 218 Plot No. 871 and he has valid documents including permit for transportation of sand for the period from 18.09.2015 to 17.09.2018.
Learned A.P.P has opposed the prayer for bail.
In the fact and circumstances of the case, the above named petitioner is directed to surrender in the Court below on or before 24.05.2018 and in the event of his arrest or surrender the Court below shall enlarge the above named petitioner on bail on furnishing bail bond of Rs. 10,000/- (Rupees ten thousand) with two sureties of the like amount each to the satisfaction of Judicial Magistrate, 1st Class, Ghatsila in connection with Bahragora P.S. Case No. 06 of 2017 corresponding to G.R. No. 14 of 2017, subject to the conditions as laid down under Section 438(2) of the Cr.P.C, subject to further condition that petitioner shall deposit Rs. 15,000/- before the President Jharkhand High Court Clerk Association on or before 16.05.2018 and thereafter President/Secretary Jharkhand High Court Clerk Association will issue the receipt of the aforesaid deposition and petitioner shall produce the receipt before the trial Court on the date of surrender, thereafter his bail bond shall be accepted.
(Anant Bijay Singh, J.) Satayendra/


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